Atlas of Genetics and Cytogenetics in Oncology and Haematology


Home   Genes    Leukemias    Solid Tumors    Cancer-Prone    Deep Insight    Case Reports    Journals   Portal    Teaching   

X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 NA
    

Internet databases and resources for cytogenetics and cytogenomics

 

Etienne De Braekeleer1, Jean Loup Huret2, Hossain Mossafa3, Katriina Hautaviita4, Philippe Dessen5

1. Haematological Cancer Genetics & Stem Cell Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, United Kingdom. 2. Medical Genetics, Dept Medical Information, University Hospital, F-86021 Poitiers, France. 3. Laboratoire CERBA, 95310 Saint Ouen l'Aumone, France. 4. (Mouse genomics, Wellcome Trust Sanger Institute) 5. UMR 1170 INSERM, Gustave Roussy, 114 rue Edouard Vaillant, F-94805 Villejuif, France.

(*) Corresponding authors : Jean Loup Huret and Philippe Dessen (*) Corresponding authors : Philippe Dessen

 

April 2016

This "Deep Insight", is a general review article and summary on Internet databases for cytogeneticists, with hyperlinks to two more detailed review articles: General resources in Genetics and/or Oncology and Cancer Cytogenomics resources, completed by a tutorial: Practical Exercices.

Content:
INTRODUCTION

2. GENERAL RESOURCES 3. CYTOGENOMICS RESOURCES
TABLE 1: Internet resources

4. PRACTICAL EXERCISE

5. DISCUSSION

Bibliography


Abstract
Databases devoted stricto sensu to cancer cytogenetics are the "Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer" (http://cgap.nci.nih.gov/Chromosomes/Mitelman), the "Atlas of Genetics and Cytogenetics in Oncology and Haematology" (http://atlasgeneticsoncology.org), and COSMIC (http://cancer.sanger.ac.uk/cosmic). However, cancer being a complex multi-step process, cytogenetics are broaden to "cytogenomics", with complementary resources, including resources on proteins and cancer. These resources are essential to both practical and theoretical knowledge in cytogenomics of cancer. Must be briefly reviewed: general databases (nucleic acid and protein sequences databases and bibliographic ones), cancer genomic portals associated to recent international integrated programs, such as TCGA or ICGC, fusion genes databases, genomic sequences and transcripts databases (with different cartography browsers), array CGH databases and structural variation databases for copy number, polymorphisms and mutation databases, databases on proteins (structure and function with implication of mutations and rearrangements), databases on diseases, databases and books on pathology, cancers, and patient associations and interfaces between science and patients. Other resources such as the International System for Human Cytogenetic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), the Human Gene Nomenclature Database (HGNC), and the Nomenclature for the description of sequence variations allow a common language. Data within the scientific/medical community should be freely available. However, most of the institutional stakeholders are now gradually disengaging, and well known databases are forced to beg or to disappear (which may happen!).

Key words: Cytogenetic, Cancer, Database, Mitelman Database, Atlas of Genetics and Cytogenetics in Oncology and Haematology, COSMIC, PubMed, GenBank, TCGA, ICGC, UniProt, OMIM, IARC, ISCN, ICD-O, HGNC.

INTRODUCTION
In each cancer case there is a genetic event present (Stratton MR et al., 2009). Cytogenetics has been a major player in understanding genetics behind cancer, providing specific keys for diagnosis and prognostic assessments, as well as enabling the sub-classification of otherwise seemingly identical disease entities (Mertens F et al., 2015). This "Deep Insight is dedicated to cytogenetics resources will highlight the various facets used in the current strategies in theoretical understanding of cancer and the consequent practical strategies in treating the disease.

Brief history
In 1914, Boveri stated that the heritable acquired characteristics of cancer cells are brought about by a disturbance of the normal chromosomal balance (Boveri, 1914). This theory was supported by a wealth of experimental data showing that cancer originates in a single cell through acquired genetic changes. The investigation in the 1950s, on ascites tumors that were induced experimentally or observed in patients tended to confirm that cytogenetic aberrations are an important and integral part of tumor development and evolution. These cytogenetic studies demonstrated that certain laws could direct neoplasia-associated chromosomal variability. Like selective pressures, where any changes in the surrounding tumor would modify the equilibrium, causing a change where the most viable chromosomal profile is prevailing in the new environment.
The importance of cytogenetics boomed since the discovery of the first chromosomal anomaly reported by Peter Nowell and David Hungerford in 1960, linking the Philadelphia (Ph) chromosome to chronic myeloid leukaemia (
CML) (Nowell and Hungerford, 1960). It was the first assessment in detecting chromosomal anomaly in human leukaemia and seemed reasonable that it was the cause of origin for CML. This discovery was the first strong assessment to Boveris theory. This observation stimulated the field to find other karyotypic anomalies in other cancers. Unfortunately, a heterogenous panel of chromosomal rearrangements was detected in what seemed to be the same cancer. This was a terrible setback for the arguments stating karyotypic anomalies as the origin of cancer. The explanation was that chromosomal rearrangements were an epiphenomenon that could appear during tumor progression without having any pathogenetic consequences.
In the 1970's the situation changed dramatically when chromosomal banding techniques invented by Caspersson and Zech (Caspersson T et al., 1970) were introduced. This process gave an option to identify individual chromosomes, which were defined by a unique banding pattern. The description of chromosomal rearrangements immediately became clearer providing more gravity to the conclusions drawn. This was a new era for cancer cytogenetics showing an increase in the numbers of aberrant human malignant and benign karyotypes.
In the 1980s, the onset of molecular genetics techniques intensely widened our understanding of the pathogenetic progression underlying the neoplastic process. These techniques provided an opportunity to characterise the chromosomal breakpoints at the molecular level and has highlighted two classes of genes implicated in these karyotypical rearrangements: the oncogenes and the tumor suppressing genes.
MYC and BCR/ABL1: One of the first oncogenes described as activated by chromosomal rearrangement is MYC, which was characterised in Burkitt lymphoma studies.
Another example is the translocation between ABL1 and BCR. Peter Nowell and David Hungerford first described a recurrent presence of an extra-chromosome in CML patients in 1960 (Nowell PCH and Hungerford DA, 1960). In 1973 Janet D. Rowley used quinacrine coloration to prove this chromosome to be the result of a translocation between chromosomes 9 and 22 (Rowley JD, 1973a). Only as late as 1982 de Klein et al. showed that the genes ABL1 and BCR were fused together giving rise to an abnormal gene (de Klein A et al., 1982). With these new techniques, each chromosome and chromosome region could be identified on the basis of their unique banding pattern, giving daylight to previously undetectable subtle rearrangements. By this technique she identified the recurrent translocation t(8;21)(q22;q22) (Rowley JD, 1973b). These findings evoked interest in the cytogenetic analysis of other haematological malignancies. The number of reported balanced rearrangements has increased, in particular translocations including t(8;14)(q24;q32), t(2;8)(p11;q24) and t(8;22)(q24;q11) in Burkitt lymphoma (Zech L et al., 1976 ; Berger R et al., 1979 ; Miyoshi I et al., 1979 ; Van Den Berghe H et al., 1979 ), t(4;11)(q21;q23) in acute lymphoblastic leukaemia (ALL) (Oshimura M et al., 1977 ) t(15;17)(q22;q21) in acute promyelocytic leukaemia (APL) (Rowley JD et al., 1977), and t(14;18)(q32;q21) in follicular lymphoma (Rowley JD et al., 1977). During this fruitful period the first specific translocation in an animal model was found, a mouse plasmacytoma, which is a B cell malignancy displaying similar characteristics to human Burkitt lymphomas (Ohno S et al., 1979).
The following decade witnessed a rise in number of results from malignant solid tumors, mainly sarcomas but also a few carcinomas. Several of the aberrations identified were as specific as the ones that were previously described in haematological cancer: t(2;13)(q36;q14) in alveolar rhabdomyosarcoma (ARMS) (Seidal T et al., 1982), t(11;22)(q24;q12) in Ewing sarcoma (Aurias A et al., 1983 ; Turc-Carel C et al., 1983), t(X;1)(p11;q21) in Kidney cancer (de Jong B et al., 1986) and t(6;9)(q23;p23) in alivary gland tumors (ACC) of the salivary glands (Stenman G et al., 1986). Evidence was showing that many benign tumors were bearing characteristic rearrangements, including reciprocal translocations such as t(3;8)(p21;q12) in salivary gland adenoma (SGA) (Mark J et al., 1980) and t(3;12)(q27;q13) in lipoma (Heim S et al., 1986 ; Turc-Carel C et al., 1986).
Although the vast majority of fusion genes are formed by balanced translocations, they can also be produced by interstitial deletions. These were first identified in the 1990s, amongst them the fusion between genes STIL (STIL/TAL1 interrupting locus) in T-ALL (Bernard O et al., 1991). Since then, many others where observed with more or less extensive deletions, duplications and/or amplifications in the breakpoint regions (Barr FG et al., 1996 ; Simon MP et al., 1997 ; Sinclair PB et al., 2000 ; Müller E et al., 2011). Gene fusion can also arise from copy number shifts like in the aforementioned fusion gene USP16/RUNX1 (ubiquitin specific peptidase 16 and runt related transcription factor 1) in chronic myelomonocytic leukemia (Gelsi-Boyer V et al., 2008) and in the fusion gene SET/NUP214 (SET nuclear proto-oncogene and nuclear pore complex protein Nup214) in T-ALL (Van Vlierberghe P et al., 2008 ; Mullighan CG et al., 2009 ; Santo EE et al., 2012 ; Plaszczyca A et al., 2014) (Figure 1).

Figure 1: Timeline of important discoveries concerning fusion genes, chromosomal rearrangements and the establishment of databases regrouping all these chromosomal abnormalities.

Technical developments
In the late 1970s, various technical developments helped in solving what molecular consequences the oncogenic chromosomal rearrangements could have. These techniques enabled the identification and characterisation of genes that were located at the breakpoints of chromosomal rearrangements. The genes implicated in MPC, Burkitt lymphoma and CML proved to be pivotal for the comprehension of the mechanism underlying chromosomal rearrangements. The engineering of fluorescence in-situ hybridization (FISH) enabled several chromosomal structures to be identified simultaneously. This significantly improved the location of breakpoints on chromosomes. It also considerably reduced the scale of which chromosomes could be observed and broadened the type of rearrangements that could be observed (cryptic rearrangements). The big advantage of the FISH technique is that it can also be used for non-dividing cells (interphase nuclei). FISH probes of a specific gene can identify new partner genes, like in the case of mixed lineage leukemia (MLL, KMT2A) gene (De Braekeleer E et al., 2009; Meyer C et al., 2013).
Although cytogenetic analyses are unquestionably crucial for the identification of fusion genes and rearrangements, there are certain limits to this technique. Firstly, revealing chromosome bandings requires having access to in-vitro living, dividing cells so that metaphases can be observed. Secondly, some tumor types can have very complex genomes which makes it difficult to understand the full story and distinguish the primary aberrations and origin of the cancer development from the bulk of the rearrangements (Speicher MR and Carter NP, 2005).
In the 1990s, the progress of high throughput tools for global genetic analyses, such as array based platforms for gene expression and copy number profiling, gave rise to new methods for observing chromosomal rearrangements. These techniques were not ideal either since balanced chromosomal rearrangements could pass undetected or the analysis of expression profiles could prove to be tricky. On the other hand, they presented a higher level of resolution than in chromosome banding and didn't require prior cell culturing (Pinkel D and Albertson DG, 2005; De Braekeleer E et al., 2014). The first novel gene fusion detected with the analysis of gene expression pattern of a tumor was the fusion of the transcription factor PAX3 gene with the nuclear receptor co-activator 1, NCOA1 gene. By focusing on genes presenting outlined values of expression, the fusions genes implicating the transmembrane protease serine 2 gene (TMPRSS2) with two genes encoding ETS transcription factors. The first is v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) and the second is ets variant 1 (ETV1) (Tomlins SA et al., 2005). It was the first report of specific fusion genes implicated and representing a major subset of a common epithelial malignancy. By using a modification of this method, other fusion genes were discovered in many tumor types, such as tenosynovial giant cell tumor, lung cancer and chondrosarcoma (West RB et al., 2006; Rikova K et al., 2007; Soda M et al., 2007; Wang L et al., 2012).
The introduction of deep sequencing technologies a few years ago gave a new insight to identify new fusions genes either at DNA or RNA level. The combination of detailed information (base pair level) and broad (genome-wide) on DNA, transcriptome, structural variants and fusion transcripts could be obtained without any prior information on the cytogenetic features of the cancer cells. The initial study using deep sequencing to detect fusion genes or chromosomal rearrangements were done on established cell lines (Campbell PJ et al., 2008). The analysis of primary samples from common cancer (Maher CA et al., 2009a; Maher CA et al., 2009b), such as carcinomas of the breast (Stephens PJ et al., 2009), colon (Cancer Genome Atlas Research Network, 2013), lung (Cancer Genome Atlas Research Network, 2012), prostate (Cancer Genome Atlas Research Network, 2014), uterus ( Cancer Genome Atlas Research Network et al., 2013) as well as leukaemias and lymphomas (Steidl C et al., 2011 ; Welch JS et al., 2011 ; Roberts KG et al., 2012), came afterward. One study draws a bridge between over several several cancers by cumulating the bioinformatics data of 4,366 cancers from 13 different tumor types that were previously studied within the Cancer Genome Atlas (TCGA) network. The outcome was the description of 8,600 different fusion transcripts (Yoshihara K et al., 2015). These results have dramatically changed the gene fusion landscape with the identification of more than 10,000 fusion genes with more than 90% of these having been identified by various deep-sequencing approaches during the last 5 years (Mitelman F et al, 2016; Huret JL et al., 2013).
The high resolution of deep sequencing gave the possibility to identify the vast majority of genes implicated in chromosomal rearrangements that would have been complicated or impossible to identify by conventional cytogenetic techniques. Indeed, 75% of the genes fusions first detected by deep sequencing are intrachromosomal and approximately 50% are between genes located in the same chromosome band (Mitelman F et al, 2016). Large majority of genes, - already described in the literature before the deep sequencing era- were embedded in extensive networks like MLL in leukaemias, EWS RNA-binding protein 1 (EWSR1) in sarcomas and rearranged during Transfection Protooncogene (RET) in carcinomas (Mitelman F et al., 2007). However, this picture has somewhat changed with the massive increase of fusion genes that were added with genome-wide studies. The fact that these studies were mainly focusing on previously uncharacterized tumor types brought a lot of new networks emerging from rarer gene fusions than leukaemias, lymphomas and sarcomas. Furthermore, carcinomas often show highly rearranged genomes, with numerous mutations at the gene and chromosome levels and it may be that the genes detected by deep sequencing are the results of chance events caused by chromosomal instability, as vast majority of fusion transcripts were associated with amplification or deletion events at the DNA level (Yoshihara K et al., 2015; Mitelman H et al, 2016; Huret JL et al., 2013; Mitelman F et al., 2007; Kalyana-Sundaram S et al., 2012). Transcription-induced gene fusion (TIGF) or Trans-TIGF, when they happen on different chromosomes, results in the fusion of transcripts from non-adjacent genes without a corresponding fusion at DNA level (Gingeras TR, 2009; Rickman DS et al., 2009; Meyer C et al., 2009 ; Hedegaard J et al., 2014). Certain have been shown to have no impact, since they were expressed in normal tissues like the fusion genes JAZF zinc finger 1 (JAZF1)/SUZ12 a polycomb repressive complex 2 subunit and PAX3/ . Others, implicating the gene MLL, to be the driving mutation (Meyer C et al., 2009).
The prognostic and treatment value of chromosomal rearrangements and mutated genes: The high correlation between recurrent gene fusions and tumor subtypes has made them the ideal maker for diagnostic purposes. This correlation is also important in treatment stratification, the best example being the different fusion of MLL in AML (Meyer C et al., 2009). The routine molecular strategy to detect these fusion genes is the use of cytogenetics, FISH, RT-PCR and deep sequencing. The mounting knowledge of the clinical importance of gene fusions, as well as various chromosomal rearrangements, has gradually led to an increasing emphasis on genetic features in the classification of tumors. The latest World Health Organisation (WHO) classification, translocation and/or gene fusion status is mandatory for the diagnosis of some types of tumors, such as "AML with t(8;21)(q22;q22), RUNX1/RUNX1T1" and "B lymphoblastic leukaemia/lymphoma with t(5;14)(q31;q32), IL3/IGH". For other cancers, such as alveolar soft part sarcoma and synovial sarcoma, it is considered as a distinctive defining element of the neoplasm (Fletcher CD, 2014; Swerdlow SH et al., 2016). Since fusion genes are diagnostic markers, they can also be used as markers for monitoring minimal residual disease following treatment (De Braekeleer E et al., 2014 ; Hokland P, Ommen HB and Hokland P, 2011). Currently, this strategy is in clinical use mainly for haematological disorders but the improvements in the detection and enrichment of circulating cancer cells and DNA suggest that solid tumors with gene fusions might also be monitored in a similar way (Crowley E et al., 2013; Karabacak NM et al., 2014; Watanabe M et al., 2014; Yu KH et al., 2014; Baccelli I et al., 2013). It is important to mention that the detection of the fusion gene can be used to monitor the progression or the relapse of the cancerous cells but it doesn't need to be an important actor in the neoplastic phenomenon, as long as they are representative and specific of the neoplastic cells (Leary RJ et al., 2010).
Research on fusion genes paved the way to develop specific drugs targeting chimeric proteins. The tyrosine kinase inhibitor Imatinib, approved in 2001, was the first drug specifically designed to target the chimeric protein BCR/ABL1 in CML (Druker BJ et al., 2001; Druker BJ et al., 2001) by blocking its kinase activity. This drug dramatically improved the lifespan and life quality of patients bearing CML. The immense success of imatinib spurred interest in developing new compounds against the chimeric proteins, all of which are kinase inhibitors. Different tumors have shown to display various fusions involving kinase-encoding genes, such as ALK, BRAF, Fibroblast growth factor receptor 3 (FGFR3), neurotrophic tyrosine kinase receptor type 1 (NTRK1), RET and ROS1 (Yoshihara K et al., 2015; ; Huret JL et al., 2013; Kohno T et al., 2013 ; Shaw AT et al., 2013). These fusion genes are occurring at low frequencies but if merged they represent a considerable number of patients. Stratification strategies considering the genotype and phenotype of the tumor would contribute greatly to identifying patients with these very promising treatment targets. Many new compounds are currently being tested in clinical models althought others have reached the Phase 1 and Phase 2 stages in clinical trials, for example, chromatin modifier such as MLL (MEN1 (Malik R et al., 2015), DOT1L (Chen CW et al., 2015), BRD4 (Dawson MA et al., 2011) or EZH2 (McCabe MT et al., 2012; Fillmore CM et al., 2015).
The need for organising data banks
Since discovering their involvement in cancer initiation, progression and evolvement, chromosomal rearrangements have triggered wide, increasing interest to understanding them better. The amount of genes involved has increased, the network underlying certain genes has been resolved and the mechanistic aspect is unravelled. Unfortunately, a lot of work has to be done before cancer has been eradicated. One of the steps is to synthesise all the information and make it available in order to increase the common knowledge of genes that are implicated and their interactions with other pathways in the cell. The importance of creating data banks and reporting various chromosomal rearrangements has been recogniced since the 80's.
1981: Human Genome Mapping
The information on chromosome modification in cancer has been included as part of the Human Genome Mapping (HGM) workshop since 1981. The provision of up-to-date information of all chromosomal rearrangements was the initial goal. This means including all case reports, which are suspected to be the starting point of tumor development or a contribution to the proliferation but also complex karyotypes with several cytogenetic anomalies or secondary modifications leading to the evolution and resistance to treatment.
The increasing number of cases, reports and the multitude of cytogenetically abnormal neoplasms made it too challenging to include everything in the database. In 1991, the HGM decided to focus only on aberrations repeatedly found as sole anomalies in a few given tumor types. As a consequence the number of recurrent changes was severely underestimated, especially in solid tumors where single anomalies are a rare finding. This illustration of chromosomal anomalies mainly represents the tip of the iceberg since the generalisation and improvements in classic cytogenetic techniques and the development of new techniques have considerably increased the number of reports of chromosomal rearrangements in different types of tumors. Several of these anomalies may be of diagnostic and prognostic importance, as well as a large amount of details of molecular analysis.
1983: Catalog of Chromosome Aberrations in Cancer
In 1983, Felix Mitelman published a colossal manuscript that was a supplement to Cytogenetics and Cell Genetics. The goal of this publication was to catalogue all known chromosomal rearrangements. The complexity of the data pushed the laboratories and institutes to adopt computerised methods to compile, revise and index the information. Many cytogeneticist, clinicians and cell biologists were in the demand for a systematic, concise and uniform presentation of material. The vast body of literature was making it complicated to evaluate if a chromosomal abnormality had been described before or not. To facilitate this process, Mitelman presented a compilation of 3,844 published and unpublished cases from colleagues or from his own laboratory. The two volumes presented all the implicated genes, chromosomes and rearrangements known. This set of two books was the first of its kind but far from the last. For several years these two volumes accompanied the bookshelves of several cytogenetists and oncologists. A re-edition of this work took place in 1985 with data of new cases and improved data of cases already described. The number of cases had now increased to a bit more than 5,000. The number of cases increased with each edition so that by the fifth edition it was composed of two large volumes of more than 4,000 pages, making it arduous to use. The sixth edition had already more than 30,000 cases in it. To make it more user friendly it was then published as a CD. The number of cases would still continue to increase and this information was not freely available. Felix Mitelman then had the idea to display the information on the Internet, rendering it freely available. In 2000, the catalogue became accessible for the public under the name Mitelman Database of Chromosome Aberrations in Cancer associated to the Cancer Genome Anatomy Project internet site and under the supervision of the National Cancer Institute (see below).
1997: Atlas of Genetics and Cytogenetics in Oncology and Haematology
How did the idea of the Atlas come about? Prognosis for leukaemia depends on the genes involved: 5 years survival rate: 6% in the inv(3)(q21q26) RPN1/MECOM leukemia, 100% in the dic(9;12)(p13;p13) PAX5/ETV6 leukemia. Treatment depends on the severity of the disease. However, thousands of genes were discovered to be implicated in cancer (14,000 unique fusion transcripts have been detected), and 1,200 types of solid tumors exist. Some cancers are frequent while many others are very rare (many with only 1 published case). This is particularly true for leukemia subtypes of which there are more than 1,000! 25,000 new publications concerning human cancer genetics are added each year to PubMed. No-one has the whole required knowledge, necessary to guide the treatment procedure in case of a rare disease. The following conclusion was made that huge databases were required to collect and summarize data on these rare diseases in order to produce meta-analyses. The Atlas has been established for that reason; to contribute to 'meta-medicine', meaning the mediation between the knowledge and the knowledge users in medicine.
Besides resources dedicated only to cytogenetics, a quick overview of resources in surrounding areas "Cancer Cytogenetics", stricto sensu, deals with chromosomes and cancer. "Cytogenetics" means "Cell Genetics" ("cyto" comes from κ υ τ ο ς, in the meaning of the term "the cell"); "Cytogenomics", as coined by Alain Bernheim, (Bernheim A et al., 2004) (from a princeps paper in French in 1998), means the "genetics -as a whole- of the cell", with complex interconnections and interactions between these operators. As is known for long, "one-gene-one-reaction" (Beadle GW, 1945) (understood today as "one-gene-one-protein"), and we can infer from "Cyto-genomics" to the terms "Cyto-transcriptome" and "Cyto-proteomics", or, in a more holistic approach, (and more simply) "Cell Biology".
Cancer is now known as being a multi-step process, with genetic events at almost each step. Therefore, the "Cancer Cytogenetics" research field should incorporate knowledge of the "Cell Biology" of normal and cancerous cells, gene fusions, mutations or copy number variation, epigenetics, protein domains, metabolic or signaling pathways, as well as consequences of these cytogenomic rearrangements and disorders in the pathogenesis of cancer, from gross and microscopic pathological presentation to patients and diseases, clinical pictures, and, even, to epidemiological data given by cancer registries.
It is useful for the cancer cytogeneticists to have an easy and quick access to databases and books of these surrounding subject areas. Therefore, besides resources of cancer cytogenetics, we will mention other resources, including resources on proteins and resources on cancer.
Presently, Internet provides access to a vast and complex network of knowledge that can make it challenging for you to find the answer to your questions. Several databases are freely accessible, but unfortunately not all of them are user friendly. We will briefly describe the main resources in the following pages.
Recent reviews on cancer databases
In complement, and not to duplicate good recent publications in the last months, some reviews on cancer databases list most of the Internet resources in the general field of cancer genomics. A review of L. Chin gives an overview of the current state if cancer genomics (L. Chin et al., 2011). Regardless of a wide spectrum of references, the topic of cytogenetic resources is absent (Pavlopoulou A et al., 2015 ; Klonowska K et al., 2016 ; Brookes AJ, Robinson PN and Brookes AJ, 2015 ; Yang Y et al., 2015 ; Niroula A and Vihinen M, 2016 ; Diehl AG and Boyle AP, 2016; Martincorena I, et al. 2015). There are also many descriptions of database (and particularly in cancer) in all special issues of Nucleic Acid Reseach (each year in January).

2. GENERAL RESOURCES
Note: a detailed description of General resources in Genetics and/or Oncology may be found at
href=http://atlasgeneticsoncology.org/Deep/General_ResourcesID20144.html

I- Bibliography
PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) is a widely used and free search engine and database of biomedical citations and abstracts, based essentially on the MEDLINE database of references on life sciences and biomedical topics. Medline is the U.S. National Library of Medicine (NLM) premier bibliographic database. PubMed Central (http://www.ncbi.nlm.nih.gov/pmc/) is an archive of biomedical and life sciences journal literature. Articles are deposited by participating journals, as well as for author manuscripts that have been submitted in compliance with the public access policies of participating research funding agencies. Scopus (http://www.scopus.com/) is a database owned by Elsevier.

II- Nomenclatures
Gene Nomenclature: The HUGO Gene Nomenclature Committee (HGNC, http://www.genenames.org/) is the authority that assigns standardised nomenclature to human genes. Nomenclature for the description of sequence variations (http://www.hgvs.org/mutnomen/) is maintained by the Human Genome Variation Society (HGVS). International System for Human Cytogenetic Nomenclature (ISCN): The ISCN is the language used to describe abnormal karyotypes. International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3): The WHO/OMS has established a code, which provides a topographical (organ) identifier and an identifier for the detailed pathology.

III- Nucleic acid, genes and protein databases
Nucleic acid databases: GenBank (http://www.ncbi.nlm.nih.gov/genbank/) is a DNA sequence database. The need to have (in parallel to the genome projects) the best representation of genomic and transcript sequences (for diverse species) has been at the origin of consensus databases (as RefSeq, UCSC, Ensembl) with several methods of optimisation. Genomic sequences and transcripts: RefSeq (http://www.ncbi.nlm.nih.gov/refseq/) maintains and curates a database of annotated genomic, transcript, and protein sequence records. Ensembl (http://www.ensembl.org/) developed a software which produces and maintains automatic annotation on selected eukaryotic genomes. The UCSC Genome Browser database (see above) is a large collection containing genome assemblies of various species. Proteins: In addition to the amino acid sequence, protein name and description with domains, these databases may provide a brief annotation information, others are only computationally analysed. These databases are the following: UniProt (http://www.uniprot.org/), a hub consisting of two sections: "TrEMBL" and "Swiss-Prot"; neXtProt (http://www.nextprot.org/db/); PhosphoSitePlus (http://www.phosphosite.org/homeAction.action), an excellent resource providing comprehensive information and tools for the study of protein post-translational modifications; PROSITE (http://prosite.expasy.org/) Pfam (http://pfam.xfam.org/) and InterPro (http://www.ebi.ac.uk/interpro/). The Atlas of Genetics and Cytogenetics in Oncology and Haematology presents highly curated paragraphs with the description of the protein, but on a restricted sample.

IV- Cards
Entrez Gene (http://www.ncbi.nlm.nih.gov/gene/) is NCBI's primary text search and retrieval system that integrates the PubMed database and molecular databases including DNA and protein sequence, structure, gene, genome, genetic variation and gene expression. Genecards (http://www.genecards.org/) is a database that provides information on all annotated and predicted human genes.

V- Genome cartography
The cartography of genes on a genome has always been a fundamental mean of representation of genomic information. With the human Genome Project, several types of viewers have been developed. To date, two sites are of first interest for human genetics: The UCSC Genome Browser website (http://genome.ucsc.edu/) contains the reference sequence for a large collection of genomes. The Genome Browser zooms and scrolls over chromosomes, "Blat" quickly maps a sequence to the genome. The UCSC Cancer Browser https://genome-cancer.ucsc.edu/proj/site/help/) allows researchers to interactively explore cancer genomics data and its associated clinical information. Ensembl (http://www.ensembl.org) generates genomic datasets and distributes created datasets and promote standards and interoperability between genomic resources.

VI- Structural variation databases
Genomic structural variation (including insertions, deletions, inversions, translocations and locus copy number changes) accounts for individual differences at the DNA sequence level in humans and can play a major role in diseases. Several databases have integrated data produced in the literature on copy number variation of DNA sequences: dbVar (http://www.ncbi.nlm.nih.gov/dbvar/), DGV - Genomic Variants (http://dgv.tcag.ca/dgv/app/home), DECIPHER (https://decipher.sanger.ac.uk/) and 1000 Genomes (http://www.1000genomes.org/).

VII- Polymorphism databases
It is important to distinguish polymorphisms due to single nucleotide (SNP) as the variability within a population and mutations acquired in a neoplastic process. The determination of variants was previously obtained by SNP arrays, but is nowadays performed by massive parallel sequencing. Polymorphism databases are: dbSNP (http://www.ncbi.nlm.nih.gov/SNP/overview.html), HAPMAP (http://hapmap.ncbi.nlm.nih.gov/index.html.en), 1000 Genomes Project (http://www.1000genomes.org/) and Exome Variant server (EVS) (http://evs.gs.washington.edu/EVS/).

VIII- Portals/Working consortiums
The primary goals of these projects are to generate catalogues of genomic abnormalities (somatic mutations, SNP genotyping, copy number variation profiling, abnormal expression of genes, epigenetic modifications) of series of genes in tumors from different cancer types. The main portals are: TCGA (http://cancergenome.nih.gov/), ICGC: (https://icgc.org/), OASIS (http://www.oasis-genomics.org/) and Firebrowse (http://firebrowse.org/).

IX- Impact on diseases
"Online Mendelian Inheritance in Man" (OMIM, http://omim.org/) is a catalog of human genes and genetic disorders; other databases providing information about human disorders and other phenotypes having a genetic component ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/intro/), MedGen (http://www.ncbi.nlm.nih.gov/medgen/), dbGaP (http://www.ncbi.nlm.nih.gov/dbgap/), SNPs3D (http://www.snps3d.org/) and GTR (http://www.ncbi.nlm.nih.gov/gtr/).

X- Pathology
Authoritative books in pathology includes clinical features, morphologic, immunohistochemical and molecular genetic features and prognosis, with a very large iconography. They are the following: the "Rosai and Ackerman's Surgical Pathology" and the "WHO/IARC Classification of Tumours series" (http://publications.iarc.fr/Book-And-Report-Series/Who-Iarc-Classification-Of-Tumours). The Armed Forces Institute of Pathology (AFIP) publishes series of the "AFIP Atlas of Tumor Pathology". The Atlas of Genetics and Cytogenetics in Oncology and Haematology provides complete description of diseases, but again on a limited sample; on the other hand, articles on genes closely related to these diseases are found, right next, in the Atlas. As a product of collaborative work, the usefulness of the Atlas is dependent on colleague participation in updating and completing it. PathologyOutlines (http://pathologyoutlines.com/) provides iconography. To be also noted, the United States and Canadian Academy of Pathology (USCAP, http://www.uscap.org/). The International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) gives ICD-O codes for each cancer, with an ICD-O3-TOPO, which provides a topographical (organ) identifier and an ICD-O3-MORPH, which provides the basic and detailed pathology.

XI- Cancer Registries
Cancer registries are organizations seeking to collect, store, analyze, and report data on various cancers for epidemiological purposes. The International Agency for Research on Cancer (IARC, http://www.iarc.fr/) is the specialized cancer agency of the World Health Organization (WHO/OMS). It publishes the "Cancer Incidence in Five Continents" series and GLOBOCAN (http://globocan.iarc.fr/Default.aspx). The International Association of Cancer Registries (IACR, http://www.iacr.com.fr/) has developed classifications (the ICD-O), guidelines for registry practices and standard definitions. quality control, consistency checks and basic analysis of data, making data comparable between registries. The European Network of Cancer Registries (ENCR, http://www.encr.eu/) has the same role in Europe as IACR has worldwide. The National Program of Cancer Registries (NPC, http://www.cdc.gov/cancer/), maintained by the Centers for disease control and prevention (CDC), collects data on cancer occurrence in the USA. The Surveillance, Epidemiology, and End Results (SEER, http://seer.cancer.gov/) is a program of the National Cancer Institute. To be cited as well, the Union for International Cancer Control (UICC, http://www.uicc.org/).

XII- Patient associations and interfaces between science and patients - freely accessible services
Associations of parents and friends of patients: These associations of parents of patients with a rare disease are precious. They give moral support and help, and offer practical guidances and information about social benefits, subsidies and day-to-day life to families affected by illness. They often establish a program of grants for research (e.g. Xeroderma Pigmentosum Society (http://www.xps.org/, Sarcoma Foundation of America (http://www.curesarcoma.org/), Union for International Cancer Control (UICC) (http://www.uicc.org/)). Interfaces between science and patients: These sites provide information for patients, including in formation on diseases, professionals for genetic counselling, laboratories, and laboratory tests: GeneTests (https://www.genetests.org/); NORD (http://rarediseases.org); Orphanet (http://www.orpha.net/).

3. CYTOGENOMICS RESOURCES
Note: a detailed description of Cancer Cytogenomics resources may be found at
Cancer Cytogenomics resources

I- Chromosome rearrangements/Hybrid genes
Mitelman Database:
The database of chromosome aberrations in cancer counts a total number of cases amounting to more than 60,000, implicating more than 10,000 gene fusions, culled from the literature and organized into distinct sub-databases: The "Cases Quick Searcher" and the "Cases Full Searcher" contain the data related to chromosomal aberrations in individual cases. The "Molecular Biology Associations Searcher" collects cases according to the gene rearrangements. The "Clinical Associations Searcher" is based on tumor characteristics, related to chromosomal aberrations and/or gene rearrangements. This free access database shows raw data and is reliable.
Atlas of Genetics and Cytogenetics in Oncology and Haematology:
The Atlas (http://atlasgeneticsoncology.org) is a peer reviewed on-line journal encyclopaedia and database with free access on the Internet. It is an integrated structure and comprises the following topics: genes, cytogenetics and clinical entities in cancer, and cancer-prone diseases. The Atlas combines various types of knowledge all on one site: genes, gene rearrangements, cytogenetics, protein domains, function, cell biology, pathways. It also contains clinical genetics, including hereditary diseases which are cancer-prone conditions, and diseases, focusing on cancers, but also listing other medical conditions. The Atlas is mainly composed of structured review articles or "cards" (original monographs written by invited authors), The Atlas contributes to the cytogenetic diagnosis and may guide treatment decision makingI
COSMIC (http://cancer.sanger.ac.uk/cosmic) is a catalog of somatic mutations in cancer. It includes all abnormalities, from single nucleotide variations to chromosome rearrangements / fusion genes.
Other resources:
chimerDB 2.0 http://biome.ewha.ac.kr:8080/FusionGene/ is a database of fusion genes with PubMed references and some information about the structure of chimeric genes. TICdb (http://www.unav.es/genetica/TICdb/) is a database of Translocation breakpoints In Cancer with the fusion sequences at the nucleotide level. ChiTARS (http://chitars.bioinfo.cnio.es/) is a database of chimeric transcripts. TCGA Fusion gene Data Portal (http://54.84.12.177/PanCanFusV2/) presents an analysis across tumor types of the TCGA program. Other resources are OMIM (http://www.omim.org/, Fusion cancer (http://donglab.ecnu.edu.cn/databases/FusionCancer/). "Cancer Cytogenetics: Chromosomal and Molecular Genetic Abberations of Tumor Cells" is a book authored by Sverre Heim and Felix Mitelman.

II- Data for SKY and FISH
Fluorescence in-situ hybridization (FISH) technique enables identification of chromosomal structures to be identified using specific probes. This significantly improves the localisation of breakpoints on chromosomes. FISH technique can also be used on non-dividing cells (interphase nuclei). The Cancer Chromosome Aberration Project (CCAP) has generated a set of BAC clones that have been mapped cytogenetically by FISH and physically by STSs to the human genome. The BAC data is integrated into various databases (http://cgap.nci.nih.gov/Chromosomes/CCAPBACClones), (http://mkweb.bcgsc.ca/bacarray/. All BAC can be located on the UCSC genome browser (http://genome.ucsc.edu). BAC from the fishClones file can be visualized on the chromosomal bands on the Atlas (http://atlasgeneticsoncology.org/Bands/). More recently, several commercial companies have developed more specific catalogs of FISH clones as oligonucleotides probes.

III- Comparative genomic hybridization (CGH) resources
This technique detects disequilibria between a disease sample and a normal sample. Several sites are repositories for these CGH/SNP profiles: GEO, http://www.ncbi.nlm.nih.gov/geo/), Array Express (http://www.ebi.ac.uk/arrayexpress/), Tumorscape (http://www.broadinstitute.org/tcga/home), MetaCGH (http://compbio.med.harvard.edu/metacgh/), CaSNP (http://cistrome.org/CaSNP/), Cell line project (http://cancer.sanger.ac.uk/cell_lines), Cancer Cell Line Encyclopedia (http://www.broadinstitute.org/ccle/home) and ArrayMap (http://www.arraymap.org)

IV- Mutation databases
The determination of variants was previously obtained by SNP arrays, but is nowadays performed by massive parallel sequencing. As a result, a huge quantity of polymorphisms and mutations in tumors, are compared to controls. The landscape of the majority of recurrent mutations is now known and can be used for diagnosis. Even in haematological malignancies, where the chromosome rearrangements have shown to bear a major role, nonetheless, it appears now that some mutations at the nucleotide level can still be very important in determining treatments in relation to patient outcome (e.g. ASXL1, ATM, BCL6, BRAF, KRAS and NRAS, CBL, CCND3, CDKN2A and CDKN2C, CEBPA, CRLF2, ETV6, FLT3, GATA2, ID3, IDH1, IDH2, IKZF1, JAK1, KIT, MYD88, NOTCH1, NPM1, RUNX1, TP53). The main mutation databases are: COSMIC (http://cancer.sanger.ac.uk/cosmic), CENSUS (http://cancer.sanger.ac.uk/census/), HGMD (http://www.hgmd.cf.ac.uk/ac/index.php), LOVD (http://www.lovd.nl/3.0/home), TCGA cBIoPortal (http://www.cbioportal.org/), ICGC Data Portal (https://dcc.icgc.org/), OASIS Portal (see above), IntOGen (http://www.intogen.org), BioMuta v2 (https://hive.biochemistry.gwu.edu/tools/biomuta/), DoCM (http://docm.genome.wustl.edu/), CIViC (https://civic.genome.wustl.edu/#/home), and ExAC (http://exac.broadinstitute.org).

TABLE 1: Internet resources


ResourceURLLocationPMIDLast Update
Nomenclature
The Human Gene Nomenclature Database (HUGO)http://www.genenames.org/(Hinxton, UK) 268423832016
International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3)http://www.who.int/classifications/icd/adaptations/oncology/en/(IARC, Lyon, Fr) -2016
Human Genome Variation Societyhttp://www.hgvs.org/(Melbourne, Aus) -2016
Cards
Atlas of Genetics in Oncology and Haematologyhttp://atlasgeneticoncology.org/(INIST-CNRS, Nancy, Fr) 231616852016
Entrez_Gene (NCBI)"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene"(NCBI, Bethesda, US) 266151912016
European Bioinformatic Institute (EBI)https://www.ebi.ac.uk(EBI, Hinxton, UK) 266737052016
EnSembl (Sanger - EBI)http://www.ensembl.org/(Sanger_EBI, Hinxton, UK) 266877192016
Gencodehttp://www.gencodegenes.org/(Sanger Institute, Hinxton, UK) 229559872015
GeneCards: human genes, proteins and diseaseshttp://www.genecards.org/ (Weizmann, Rehovot, Is) 270483492016
SOURCEhttp://source-search.princeton.edu/cgi-bin/source/sourceSearch(Princeton, US) -2015
AceViewhttp://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/(NCBI, Bethesda, US) 169258342012
GENATLAS http://www.dsi.univ-paris5.fr/genatlas(Imagine, Paris, Fr) 98350182016
WikiGeneshttp://www.wikigenes.org/(De) 18728691-
H-invDBhttp://www.h-invitational.jp/hinv/ahg-db/index.jsp(Tokai University,Ja) 231976572015
Cancer Portals
Genomic Data Commonshttps://gdc.nci.nih.govNCI, Bethesda, US)  2016
ICGC Data Portalhttps://dcc.icgc.org/(OICR, Ontario, Ca) 219305022015
TCGA cBIoPortalhttp://www.cbioportal.org/public-portal/(MSKCC, New_York, US) 268523302016
Broad Tumor Portalhttp://www.tumorportal.org/(Broad Institute, Boston, US) 243903502014
Firebrowse GDAChttp://firebrowse.org/(Broad Institute, Boston, US) -2016
GTEx Portal http://www.gtexportal.org/home/(Broad Institute, Boston, US) 259540012016
Integrative Onco Genomics (intOgen)http://www.intogen.org/(Barcelone, Es) 201110332014
OASIS Portal http://www.oasis-genomics.org/(Pfizer, US) -2015
Cancer Browser (UCSC)https://genome-cancer.ucsc.edu/(San Diego, US) 253924082015
canSARhttps://cansar.icr.ac.uk/(ICR, London, UK) 266737132016
IHEC Data Portalhttp://epigenomesportal.ca/ihec/(Mc Gill, Ca) -
Genomic and Cartography
Human Genome Browser (UCSC)http://genome.ucsc.edu/goldenPath(San Diego, US) 265902592015
Ensembl Map view (Sanger _EBI)http://www.ensembl.org/(Sanger_EBI, Hinxton, UK) 266877192015
NCBI Map Viewer : Homo_sapiens genome view http://www.ncbi.nlm.nih.gov/mapview/(NCBI, Bethesda, US) 266151912015
CGAP : Bac cloneshttp://cgap.nci.nih.gov/Chromosomes/CCAP_BAC_Clones(NCI, Bethesda, US) --
Gene, transcription and regulation
NIH Genetic Sequence database (GenBank)http://www.ncbi.nlm.nih.gov/Genbank/GenbankOverview.html(NCBI, Bethesda, US) 266151912016
Reference Sequence database (RefSeq)http://www.ncbi.nlm.nih.gov/refseq/(NCBI, Bethesda, US) 266151912016
Encyclopedia of DNA Elements (ENCODE)https://www.encodeproject.org/(NHGRI, US) 269620252016
The Consensus CDS Project (CCDS)http://www.ncbi.nlm.nih.gov/projects/CCDS/CcdsBrowse.cgi(UCSC, San Diego, US) 242179092016
Unigene (NCBI)http://www.ncbi.nlm.nih.gov/UniGene/(NCBI, Bethesda, US) 266151912016
ASG - Alternative Splicing Gallery (ASG)http://statgen.ncsu.edu/asg/(NCSU, US) 152924482004
Gene Expression Atlas (EBI)http://www.ebi.ac.uk/gxa/The ArrayExpress (EBI, Hinxton, UK) 264813512016
Gene Expression Omnibus (NCBI)http://www.ncbi.nlm.nih.gov/geo(NCBI, Bethesda, US) 270080112016
Exploration of Expression Compendium (SEEK)http://seek.princeton.edu/(Princeton, US) 255818012015
Multi Experiment Matrix (MEM)http://biit.cs.ut.ee/mem/index.cgi(Tartu, Est) 199615992012
BioGPShttp://biogps.org/(Scripps, US) 265785872016
Human Epigenomehttp://www.epigenome.org/(Sanger, UK) 15550986
http://ihec-epigenomes.org/http://ihec-epigenomes.org/(Mc Gill, Ca)
Protein : sequence, function, domain, 3D structure
Atlas of Genetics in Oncology and Haematologyhttp://atlasgeneticoncology.org/(INIST-CNRS, Nancy, Fr) 231616852016
UniProthttp://www.uniprot.org/(EBI, Hinxton, UK) 260880532016
SwissProt http://www.expasy.ch/sprot/ (SIB, Geneve, Ch) 260880532016
Swiss-VARhttp://swissvar.expasy.org/(SIB, Geneva, Ch) 201068182016
NextProt http://www.nextprot.org/(SIB, Geneva, Ch) 255933492016
ENZYMEhttp://us.expasy.org/enzyme/(SIB, Geneve, Ch) 105922552016
PhosPhoSitePlushttp://www.phosphosite.org/(Danvers, US) 255149262014
Prosite: Protein signaturehttp://www.expasy.ch/prosite/(SIB, Geneva, Ch) 231616762016
Protein families (PFAM)http://pfam.xfam.org/(Sanger, Hinxton, UK) 266737162015
A Conserved Domain Database (CDD)http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml(NCBI, Bethesda, US) 189846182015
Domain mapping of disease mutations (DMDM)http://bioinf.umbc.edu/dmdm/(Baltimore, US) 206859562014
Protein domain (PRODOM) http://prodom.prabi.fr/(PRABI, Lyon, Fr) 122300332015
Protein Data Bank (PDB)http://www.rcsb.org/(San Diego, US) 254283752016
Pictorial database of 3D structures (PDBsum)http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/(EBI, Hinxton, UK) 156081932016
Jena Library of Biological Macromolecules (IMB)http://jenalib.fli-leibniz.de/(Jena, De) 117523082015
Structural Biology Knowledgebase (SBKB)http://sbkb.org/pdbid(Rutgers, US) 214724362016
Structural Classification of Proteins (SCOP)http://scop.berkeley.edu/search/?ver=2.05(Berkeley, US) 243048992015
Classification of protein structures (CATH)http://www.cathdb.info/(UCL, London, UK) 253484082013
Human Protein Atlas http://www.proteinatlas.org/(Upsalla, Su) 215724092016
Human Protein Reference Database (HPRD)http://www.hprd.org/(John Hopkins, Baltimore, US) 221591322010
Differentially Expressed Proteins in Human Cancer (dbDEPC)http://dbdepc.biosino.org/(Shanghai, Cn) 220962342011
Protein Interaction databases
Database of Interacting Proteins (DIP)http://dip.doe-mbi.ucla.edu/dip/Guide.cgi(UCLA, US) 146814542014
Molecular Interaction Database (IntAct)http://www.ebi.ac.uk/intact/index.jsp(EBI, Hinxton, UK) 221212202016
Functional coupling (FunCoup)http://funcoup.sbc.su.se/(KTH, Stockholm, Su) 241857022013
Biological General Repository for Interaction Datasets (BioGRID)http://thebiogrid.org/(Toronto, Ca) 267299132016
Ontologies - Pathways
Gene Ontologyhttp://www.geneontology.org/- 253783362016
QuickGOhttp://www.ebi.ac.uk/QuickGO/(EBI, Hinxton, UK) 197449932016
Kegg Kyoto Encyclopedia of Genes and Genomes (KEGG)http://www.genome.jp/kegg/ (Kyoto, Jp) 264764542016
BioCarta Pathwayshttp://cgap.nci.nih.gov/Pathways/BioCarta_Pathways(NCI, Bethesda, US) -
Reactomehttp://www.reactome.org/"(OICR, ca; New-York, US; EBI, UK)" 266564942016
Network Data Exchange (NDEx )http://www.ndexbio.org/(University of California, US) 265946632016
Atlas of Cancer Signalling Networks global map (ACSN)http://acsn.curie.fr/(Curie, Paris, Fr) 261926182013
WikiPathwayshttp://www.wikipathways.org/index.php/ 220962302016
Orthology - Evolution
The Hierarchical Catalog of Eukaryotic Orthologs (OrthoDB)http://cegg.unige.ch/orthodb5(Univ. Geneva, Ch) 254283512011
TREEFAM:http://www.treefam.org/(EBI, Hinxton, uk) 241946072013
Homologene (NCBI)http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=homologene(NCBI, Bethesda, US) 266151912016
Gene Sorter (UCSC)http://genome.ucsc.edu/goldenPath/help/hgNearHelp.html(UCSC, San Diego, US) 158674342016
Ortholog groups with inparalogs (InParanoid)http://inparanoid.sbc.su.se/cgi-bin/index.cgi(Stockholm, Su) 254299722013
HOVERGENhttp://www.prabi.fr/spip.php?article38(Prabi: Lyon, Fr) 195347522009
Gene fusions - Chromosomal Rearrangments
Atlas of Genetics in Oncology and Haematologyhttp://atlasgeneticoncology.org/(INIST-CNRS, Nancy, Fr) 231616852016
Mitelman Database of Chromosome Aberrations in Cancerhttp://cgap.nci.nih.gov/Chromosomes/Mitelman(NCBI, Bethesda, US) -2016
Catalog of somatic mutations in Cancer (COSMIC)http://cancer.sanger.ac.uk/cosmic(Sanger Center, Hinxton, UK) 253555192016
Database of Translocation breakpoints In Cancer (TICdb)http://www.unav.es/genetica/TICdb/(Univ Navarre, Sp) 172574202013
ChiTARShttp://chitars.bioinfo.cnio.es/(Barcelone, Es) 254143462014
TCGA Fusion gene Data Portal http://54.84.12.177/PanCanFusV2/(MDACC, Houston, US) 255005442014
Fusion Cancer http://donglab.ecnu.edu.cn/databases/FusionCancer/index.html(Beijing, Cn) 262156382014
ChimerDB 2.0http://biome.ewha.ac.kr:8080/FusionGene/(Ewha Womans University, Kr) 199067152010
Database of Chromosomal Rearrangements In Disease (dbCRID)http://c1.accurascience.com/dbCRID/(Houston, US) 210513462010
arrayMap - genomic arrays for copy number profiling in human cancerhttp://www.arraymap.org/(UZH-SIB, Zurich, Ch) 254283572016
CONAN : Cell lines Project: Copy Number Analysishttp://www.sanger.ac.uk/cgi-bin/genetics/CGP/conan/search.cgi(Sanger Center, Hinxton, UK) -2016
Polymorphisms : SNP, mutations
Single Nucleotide Polymorphism (dbSNP)http://www.ncbi.nlm.nih.gov/SNP/overview.html(NCBI, Bethesda, Us) 266151912016
The International HapMap Projecthttp://www.hapmap.org/index.html.en(NCBI, Bethesda, US) 208114512011
A Deep Catalog of Human Genetic Variation (1000 Genomes)http://www.1000genomes.org/(EBI, Hinxton, UK) 264322452016
Exome Aggregation Consortium (ExAC)http://exac.broadinstitute.org/(Broad, US) 267919502016
Exome Variant server (EVS)http://evs.gs.washington.edu/(Washington, US) 226047202014
ICGC Data Portalhttp://dcc.icgc.org/(OICR, Ontario, Ca) 219305022015
TCGA Copy Number Portalhttp://www.broadinstitute.org/tcga/home(Broad Institute, Boston, US) 201649202013
Cancer Gene Censushttp://cancer.sanger.ac.uk/cancergenome/projects/census/(Sanger Center, Hinxton, UK) 149938992016
Catalog of somatic mutations in Cancer (COSMIC)http://www.sanger.ac.uk/perl/CGP/cosmic(Sanger Center, Hinxton, UK) 253555192016
Leiden Open Variation Database (LOVD 3.0)http://www.lovd.nl/3.0/home(Leiden, Ne) 215203332016
BioMuta v2https://hive.biochemistry.gwu.edu/tools/biomuta/(George Washington Univ, Washington DC, US) 246672512014
DoCM Database of curated mutations http://docm.genome.wustl.edu/(WUSTL, US) -2016
CIViC Clinical Interpretations of Variants in Cancerhttps://civic.genome.wustl.edu/#/home(WUSTL, US) -2016
NCG5http://ncg.kcl.ac.uk/265161862016
Cancer3Dhttp://www.cancer3d.org/253924152015
Integrative Onco Genomics (intOgen)http://www.intogen.org(Barcelona, Es) 201110332014
Human Gene Mutation Database (HGMD ) (*)http://www.hgmd.cf.ac.uk/ac/(Institute of Medical Genetics, Cardiff, UK) 240779122016
Database of genomic structural variation (dbVar)http://www.ncbi.nlm.nih.gov/dbvar/(Bethesda, NCBI, US) 266151912016
DGV -Genomic Variantshttp://dgv.tcag.ca/dgv/app/home (Toronto, Ca) 241745372015
DECIPHERhttps://decipher.sanger.ac.uk/Sanger Centre, Hinxton, UK) 193448732016
SNPs3Dhttp://www.snps3d.org/(UMD, US) 164124612006
Human Genome Variant Societyhttp://www.hgvs.org/content/guidelines(Melbourne, Aus) 269311832016
Diseases
Atlas of Genetics in Oncology and Haematologyhttp://atlasgeneticoncology.org/(INIST-CNRS, Nancy, Fr) 231616852016
Online Mendelian Inheritance in Man (OMIM)http://www.omim.org(John Hopkins, Baltimore, US) 254283492016
Human medical genetics (MedGen)http://www.ncbi.nlm.nih.gov/medgen/(NCBI, Bethesda, US) 266151912016
Database of Genotypes and Phenotypes (dbGaP)http://www.ncbi.nlm.nih.gov/gap/(NCBI, Bethesda, US) 266151912016
Human variations and phenotypes (ClinVar)http://www.ncbi.nlm.nih.gov/clinvar/intro(NCBI, Bethesda, US) 270374892016
The Genetic Testing Registry (GTR)http://www.ncbi.nlm.nih.gov/gtr/(NIH, Bethesda, US) 231932752016
A medical genetics information resource (GeneTests)https://www.genetests.org/disorders/(Elmwood Park, New Jersey, US) 180735872016
HuGE Navigatorhttps://phgkb.cdc.gov/HuGENavigator/home.do(CDC, Atlanta, US) -2016
Database of rare diseases and orphan drugs (ORPHANET)http://www.orpha.net/(INSERM, Paris, Fr) 126558252016
National Organization for Rare Diseases (NORD)http://rarediseases.orgUS -2016
Clinical trial, drugs and therapy
BioCentury BCIQhttp://www.biocentury.com/(Redwood City, US) -2016
The Drug Gene Interaction Database (DgiDB)http://dgidb.genome.wustl.edu/(WUSTL, US) 265318242016
Comparative Genomics Database (CTD )http://ctd.mdibl.org/(NC State Univ, US) 253263232016
Pharmacogenomics. Knowledgehttp://www.pharmgkb.org/(Stanford, US) -2016
Genomics of Drug Sensitivity in Cancer (Wellcome Trust)http://www.cancerrxgene.org/(Sanger, Hinxton, UK) 231807602012
Clinical Trialhttp://clinicaltrials.gov/ct2/search/browse?brwse=intr_cat_ANeo(NIH, Bethesda, US) -2016
Cancer registries
International Agency for Research on Cancerhttp://www.iarc.fr/(Lyon, Fr) -2016
International Association of Cancer Registries http://www.iacr.com.fr/(Lyon, Fr) -2016
European Network of Cancer Registries http://www.encr.eu/ -2016
National Program of Cancer Registries http://www.cdc.gov/cancer/US -2016
Union for International Cancer Control http://www.uicc.org/US -2016
Bibliography, Data mining
COREMINEhttp://www.coremine.com/medical/#search(Oslo, No) -2016
Information Hyperlinked over Proteins (iHOP)http://www.ihop-net.org/UniPub/iHOP/(MSKCC, New-York, US) 184286782016
Gene Interactions in Cancer (ZODIAC)http://www.compgenome.org/ZODIAC(Evanston, US) 259563562016
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed(NIH, Bethesda, UrSs) 266151912016
Pubmed Centralhttp://www.ncbi.nlm.nih.gov/pmc(NIH, Bethesda, US) 266151912016
PubChemhttp://pubchem.ncbi.nlm.nih.gov/(NIH, Bethesda, US) 266151912016
GoPubMedhttp://www.gopubmed.org/web/gopubmed/(NIH, Bethesda, US) 266151912016

© Atlas of Genetics and Cytogenetics in Oncology and Haematology
indexed on : Thu Jun 9 10:26:41 MEST 2016
4. PRACTICAL EXERCISE
Practical Exercices

5. DISCUSSION
We have briefly discussed the various databases useful for clinicians, students, and researchers in finding answers to their questions and in determining which field in cancer research still needs to be studied. Only a handful of databases or portals take the cytogenetic information into consideration although being one of the first observation points confirming that the cell has transformed into a cancerous cell. Over the years (1960-2016), chimeric genes and fusion proteins have been discovered mainly by cytogenetic means. This has led to understanding of major cancerogenetic processes, and, later on, to the concept of treatments targets for many cases. Cytogenetics, or rather, cytogenomics of cancer, is a major contributor for the concept of "personalized medicine for cancer", together with other tools for other mechanisms. Dorothy Warburton wrote her colleagues in the American Cytogenetics Forum List: Subject: Is Cytogenetics Dying? "I have been told by my department chairman and other advisers that cytogenetics is dying at least three times: in 1968 (just before banding), in 1984 (just before FISH) and in 2001 (just before microarrays) (...) Chromosome changes are being recognised as the cause of more and more abnormalities, not fewer. This is true for both cancer and constitutional cytogenetics (...) There are also a great many basic facts about how chromosome abnormalities originate that we know little about and that are great fields for research".
Besides what is known, there is still a lot to learn about how a mutation or a chromosomal rearrangement is influencing the cellular mechanism.
A decade ago, conventional wisdom was that the expressed genes were regulating all the processes in the cell and the rest of the genome was considered as "junk DNA". The main purpose of this junk DNA was to act as a buffer and to protect the coding part from any kind of aggression. Progressively, we realized that the mutations happening outside the coding regions could influence the expression of nearby genes or in some cases several kilobases away. These mutations were located in regulatory regions recruiting or blocking transcription factors binding DNA.
More elements were found in the "junk DNA" with the discovery of the microRNAs (miRNA) and long non-coding RNAs (lincRNA). Both proved influential in regulating the genome and the expression of several genes. These miRNA and lincRNA are deregulated during the carcinogenic process and studies have demonstrated that by re-establishing the normal expression of one or few miRNAs or lincRNAs, the cancer cells transform back into normal cells. Afterwards, it was observed that genes which were not mutated could be silenced and vice versa. This was due to the epigenetic regulation of the genes and the recruitment of epigenetic regulators in different regions of the genome. The cancerous cells actively modify their own epigenetic marks on the genome to increase the expression of stemness genes providing them with proliferative or resistance advantages.
The chromosome structure in the nucleus is dependent on many variables modulating the chromatin interactions across the whole genome. Techniques like chromatin conformation capture and interphase fluorescent in situ hybridization (FISH) detect spatial associations between specific genes. In the future, they are likely to be the techniques for detecting abnormalities associated with progression of tumours. Similarly, when modifying the 3D structure of chromosomes, it has been demonstrated that the disruption of chromosome neighbourhoods via mutations in insulated neighbourhood boundaries (cohesin CTCF interactions) activates proto-oncogenes in cancer cells (Hnisz D et al., 2016).
As mentioned above, the last build of the human genome (GRCh38, Dec 2013) is more precise than the previous ones and takes into account more haplotypes shared by parts of the human population. Nevertheless, due to a great part of repeated sequences of various classes (50% of the genome), there are some gene families only present at low frequencies, e.g. the insertions of endogenous retroviruses, which represent 8% of the genome and are mostly not localized on GRCh38 for a great part (Wildschutte JH et al., 2016). We need to recognize that such a diversity may be a factor leading to susceptibility to cancer or other diseases.
There is an increasing realisation that our environment, our nutrition and our way of life have an impact on how our cells mutate and how theses mutations are repaired or how the abnormal cells are eliminated by our organism.
It is difficult to fully understand and appreciate the complexity of the cellular mechanism and the various levels that can be deregulated in cancer makes. The use of databases makes this process easier as it condenses the complex information and provides links to other relevant databases providing even more specialized information.
Sequencing of thousands of genomes of patients bearing different types of tumours and genomes of normal persons, generates a huge amount of data demonstrating the complexity of tumours. Focus points are the sensitive parts of the genome which are more prone to be mutated, the expression profiles, the function of the proteins, the pathways the proteins are located in, and the protein interactions (in normal cases and abnormal cases). The same is done at the single cell level, bringing a huge amount of data demonstrating the heterogeneity of the tumour and the interaction with the niche. In principle, this wealth of integrated genomic data and clinical information could reveal the genetic bases of cancer, inherited diseases, and drug responses-illnesses and remedies that have touched nearly every person and family across the globe. This will constitute the databases of tomorrow.
Interpreting these data requires a larger evidence base than any one party alone can develop. However, existing technologies, regulations and approaches are currently not designed for sharing and interpreting this wealth of information effectively, especially across diseases and nations. Databases will need to integrate information increasingly in the upcoming years but also stay interoperable with other databases. This corroborates the idea of having a common nomenclature and language to avoid mistakes but also have common and reliable information (http://www.aacr.org/Research/Research/Pages/aacr-project-genie.aspx#.Vx_mi8f90qx; https://genomicsandhealth.org) in the same manner in which the Working Group for Planetary System Nomenclature maintains the "astronomical naming conventions and planetary nomenclature", any scientific field, confronted with the exponential growth of data, has to create a system of classification and thesaurus for naming of new items, and nosological definitions. This often implies the use of a structured grammar and census of accepted terms/objects.
Resources such as the International System for Human Cytogenetic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), the Human Gene Nomenclature Database (HGNC), and the Nomenclature for the description of sequence variations (from the HGVS) are indispensable research tools allowing a common language.
Interoperability: In the context of interoperability between recent cancer projects, such as TCGA and ICGC as well as all data produced by thousands laboratories or hospitals, the Global Alliance for Genomics and Health (Global Alliance, https://genomicsandhealth.org/) (Lawler M et al., 2015) was created to accelerate the potential of genomic medicine. This association brings together over 375 leading institutions working together to generate a common framework of harmonized approaches in enabling the data sharing. In particular, it works to establish interoperable technical standards (standardised language and tools) to management of genomic and clinical data and for a better representation of genotype-phenotype associations.
Open data (open source, open hardware, open content, and open access) is a concept so dear to many, in particular in the medical and scientific world. This concept has recently had a renewed vigour with/since the advent of the Internet. Maintaining the data open and free remains a daily struggle. Everything has a cost, even a free database, from upkeeping and updating the database to providing for the scientific staff necessary to produce expertized data.
Data should remain freely available. However, a business model remains to be established. Although economic investment by the public sector would benefit the whole of mankind, as well as economically profitable in the end, most of the institutional stakeholders are gradually disengaging, and well-known databases are forced to beg for funds (see recent examples, useless to cite them!) or to disappear. This would be a regrettable drawback for the scientific and medical community - yet it may still happen.

Bibliography

The cancer genome
Stratton MR, Campbell PJ, Futreal PA
Nature 2009 Apr 9;458(7239):719-24
PMID 19360079
 
The emerging complexity of gene fusions in cancer
Mertens F, Johansson B, Fioretos T, Mitelman F
Nat Rev Cancer 2015 Jun;15(6):371-81
PMID 25998716
 
Zur Frage der Enstehung maligner Tumoren
Boveri T.
1914 Gustav Fischer
 
A minute Chromosome in Human Chronic Ganulocytic Leukemia
Nowell PC, Hungerford DA
Science 1960 132:1497
 
Fluorescent labeling of chromosomal DNA: superiority of quinacrine mustard to quinacrine
Caspersson T, Zech L, Modest EJ
Science 1970 Nov 13;170(3959):762
PMID 5479635
 
Identificaton of a translocation with quinacrine fluorescence in a patient with acute leukemia
Rowley JD
Ann Genet 1973 Jun;16(2):109-12
PMID 4125056
 
A cellular oncogene is translocated to the Philadelphia chromosome in chronic myelocytic leukaemia
de Klein A, van Kessel AG, Grosveld G, Bartram CR, Hagemeijer A, Bootsma D, Spurr NK, Heisterkamp N, Groffen J, Stephenson JR
Nature 1982 Dec 23;300(5894):765-7
PMID 6960256
 
Letter: A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining
Rowley JD
Nature 1973 Jun 1;243(5405):290-3
PMID 4126434
 
Characteristic chromosomal abnormalities in biopsies and lymphoid-cell lines from patients with Burkitt and non-Burkitt lymphomas
Zech L, Haglund U, Nilsson K, Klein G
Int J Cancer 1976 Jan 15;17(1):47-56
PMID 946170
 
A new translocation in Burkitt's tumor cells
Berger R, Bernheim A, Weh HJ, Flandrin G, Daniel MT, Brouet JC, Colbert N
Hum Genet 1979;53(1):111-2
PMID 535896
 
2/8 translocation in a Japanese Burkitt's lymphoma
Miyoshi I, Hiraki S, Kimura I, Miyamoto K, Sato J
Experientia 1979 Jun 15;35(6):742-3
PMID 467575
 
Variant translocation in Burkitt lymphoma
Van Den Berghe H, Gosseye CP, Englebienne V, Cornu G, Sokal G
Cancer Genetics and Cytogenetics 1960, 1; 9-14
 
Chromosomes and causation of human cancer and leukemia
Oshimura M, Freeman AI, Sandberg AA
XXVI Binding studies in acute lymphoblastic leukemia (ALL)
PMID 268996
 
15/17 translocation, a consistent chromosomal change in acute promyelocytic leukaemia
Rowley JD, Golomb HM, Dougherty C
Lancet 1977 Mar 5;1(8010):549-50
PMID 65649
 
Chromosome abnormalities in poorly differentiated lymphocytic lymphoma
Fukuhara S, Rowley JD, Variakojis D, Golomb HM
Cancer Res 1979 Aug;39(8):3119-28
PMID 582296
 
Nonrandom chromosome changes involving the Ig gene-carrying chromosomes 12 and 6 in pristane-induced mouse plasmacytomas
Ohno S, Babonits M, Wiener F, Spira J, Klein G, Potter M
Cell 1979 Dec;18(4):1001-7
PMID 519762
 
Alveolar rhabdomyosarcoma: a cytogenetic and correlated cytological and histological study
Seidal T, Mark J, Hagmar B, Angervall L
Acta Pathol Microbiol Immunol Scand A 1982 Sep;90(5):345-54
PMID 7148452
 
[Translocation of chromosome 22 in Ewing's sarcoma]
Aurias A, Rimbaut C, Buffe D, Dubousset J, Mazabraud A
C R Seances Acad Sci III 1983;296(23):1105-7
PMID 6416623
 
[Chromosomal translocation (11; 22) in cell lines of Ewing's sarcoma]
Turc-Carel C, Philip I, Berger MP, Philip T, Lenoir G
C R Seances Acad Sci III 1983;296(23):1101-3
PMID 6416622
 
Cytogenetics of a renal adenocarcinoma in a 2-year-old child
de Jong B, Molenaar IM, Leeuw JA, Idenberg VJ, Oosterhuis JW
Cancer Genet Cytogenet 1986 Mar 15;21(2):165-9
PMID 3004698
 
6q- and loss of the Y chromosome--two common deviations in malignant human salivary gland tumors
Stenman G, Sandros J, Dahlenfors R, Juberg-Ode M, Mark J
Cancer Genet Cytogenet 1986 Aug;22(4):283-93
PMID 3015376
 
The mixed salivary gland tumor A normally benign human neoplasm frequently showing specific chromosomal abnormalities.
Mark J, Dahlenfors R, Ekedahl C, Stenman G
Cancer Genetics and Cytogenetics 1980 2, 231-24
 
Reciprocal translocation t(3;12)(q27;q13) in lipoma
Heim S, Mandahl N, Kristoffersson U, Mitelman F, Rser B, Rydholm A, Willén H
Cancer Genet Cytogenet 1986 Dec;23(4):301-4
PMID 3779626
 
Cytogenetic studies of adipose tissue tumors
Turc-Carel C, Dal Cin P, Rao U, Karakousis C, Sandberg AA
I A benign lipoma with reciprocal translocation t(3;12)(q28;q14)
PMID 3779624
 
Two site-specific deletions and t(1;14) translocation restricted to human T-cell acute leukemias disrupt the 5' part of the tal-1 gene
Bernard O, Lecointe N, Jonveaux P, Souyri M, Mauchauffé M, Berger R, Larsen CJ, Mathieu-Mahul D
Oncogene 1991 Aug;6(8):1477-88
PMID 1886719
 
In vivo amplification of the PAX3-FKHR and PAX7-FKHR fusion genes in alveolar rhabdomyosarcoma
Barr FG, Nauta LE, Davis RJ, Schäfer BW, Nycum LM, Biegel JA
Hum Mol Genet 1996 Jan;5(1):15-21
PMID 8789435
 
Deregulation of the platelet-derived growth factor B-chain gene via fusion with collagen gene COL1A1 in dermatofibrosarcoma protuberans and giant-cell fibroblastoma
Simon MP, Pedeutour F, Sirvent N, Grosgeorge J, Minoletti F, Coindre JM, Terrier-Lacombe MJ, Mandahl N, Craver RD, Blin N, Sozzi G, Turc-Carel C, O'Brien KP, Kedra D, Fransson I, Guilbaud C, Dumanski JP
Nat Genet 1997 Jan;15(1):95-8
PMID 8988177
 
Large deletions at the t(9;22) breakpoint are common and may identify a poor-prognosis subgroup of patients with chronic myeloid leukemia
Sinclair PB, Nacheva EP, Leversha M, Telford N, Chang J, Reid A, Bench A, Champion K, Huntly B, Green AR
Blood 2000 Feb 1;95(3):738-43
PMID 10648381
 
FUS-CREB3L2/L1-positive sarcomas show a specific gene expression profile with upregulation of CD24 and FOXL1
Möller E, Hornick JL, Magnusson L, Veerla S, Domanski HA, Mertens F
Clin Cancer Res 2011 May 1;17(9):2646-56
PMID 21536545
 
Genome profiling of chronic myelomonocytic leukemia: frequent alterations of RAS and RUNX1 genes
Gelsi-Boyer V, Trouplin V, Adélaïde J, Aceto N, Remy V, Pinson S, Houdayer C, Arnoulet C, Sainty D, Bentires-Alj M, Olschwang S, Vey N, Mozziconacci MJ, Birnbaum D, Chaffanet M
BMC Cancer 2008 Oct 16;8:299
PMID 18925961
 
The recurrent SET-NUP214 fusion as a new HOXA activation mechanism in pediatric T-cell acute lymphoblastic leukemia
Van Vlierberghe P, van Grotel M, Tchinda J, Lee C, Beverloo HB, van der Spek PJ, Stubbs A, Cools J, Nagata K, Fornerod M, Buijs-Gladdines J, Horstmann M, van Wering ER, Soulier J, Pieters R, Meijerink JP
Blood 2008 May 1;111(9):4668-80
PMID 18299449
 
Rearrangement of CRLF2 in B-progenitor- and Down syndrome-associated acute lymphoblastic leukemia
Mullighan CG, Collins-Underwood JR, Phillips LA, Loudin MG, Liu W, Zhang J, Ma J, Coustan-Smith E, Harvey RC, Willman CL, Mikhail FM, Meyer J, Carroll AJ, Williams RT, Cheng J, Heerema NA, Basso G, Pession A, Pui CH, Raimondi SC, Hunger SP, Downing JR, Carroll WL, Rabin KR
Nat Genet 2009 Nov;41(11):1243-6
PMID 19838194
 
Oncogenic activation of FOXR1 by 11q23 intrachromosomal deletion-fusions in neuroblastoma
Santo EE, Ebus ME, Koster J, Schulte JH, Lakeman A, van Sluis P, Vermeulen J, Gisselsson D, Øra I, Lindner S, Buckley PG, Stallings RL, Vandesompele J, Eggert A, Caron HN, Versteeg R, Molenaar JJ
Oncogene 2012 Mar 22;31(12):1571-81
PMID 21860421
 
Fusions involving protein kinase C and membrane-associated proteins in benign fibrous histiocytoma
Pńaszczyca A, Nilsson J, Magnusson L, Brosjö O, Larsson O, Vult von Steyern F, Domanski HA, Lilljebjörn H, Fioretos T, Tayebwa J, Mandahl N, Nord KH, Mertens F
Int J Biochem Cell Biol 2014 Aug;53:475-81
PMID 24721208
 
FLNA, a new partner gene fused to MLL in a patient with acute myelomonoblastic leukaemia
De Braekeleer E, Douet-Guilbert N, Morel F, Le Bris MJ, Meyer C, Marschalek R, Férec C, De Braekeleer M
Br J Haematol 2009 Sep;146(6):693-5
PMID 19622092
 
The MLL recombinome of acute leukemias in 2013
Meyer C, Hofmann J, Burmeister T, Gröger D, Park TS, Emerenciano M, Pombo de Oliveira M, Renneville A, Villarese P, Macintyre E, Cavé H, Clappier E, Mass-Malo K, Zuna J, Trka J, De Braekeleer E, De Braekeleer M, Oh SH, Tsaur G, Fechina L, van der Velden VH, van Dongen JJ, Delabesse E, Binato R, Silva ML, Kustanovich A, Aleinikova O, Harris MH, Lund-Aho T, Juvonen V, Heidenreich O, Vormoor J, Choi WW, Jarosova M, Kolenova A, Bueno C, Menendez P, Wehner S, Eckert C, Talmant P, Tondeur S, Lippert E, Launay E, Henry C, Ballerini P, Lapillone H, Callanan MB, Cayuela JM, Herbaux C, Cazzaniga G, Kakadiya PM, Bohlander S, Ahlmann M, Choi JR, Gameiro P, Lee DS, Krauter J, Cornillet-Lefebvre P, Te Kronnie G, Schäfer BW, Kubetzko S, Alonso CN, zur Stadt U, Sutton R, Venn NC, Izraeli S, Trakhtenbrot L, Madsen HO, Archer P, Hancock J, Cerveira N, Teixeira MR, Lo Nigro L, Möricke A, Stanulla M, Schrappe M, Sedék L, Szczepański T, Zwaan CM, Coenen EA, van den Heuvel-Eibrink MM, Strehl S, Dworzak M, Panzer-Grümayer R, Dingermann T, Klingebiel T, Marschalek R
Leukemia 2013 Nov;27(11):2165-76
PMID 23628958
 
The new cytogenetics: blurring the boundaries with molecular biology
Speicher MR, Carter NP
Nat Rev Genet 2005 Oct;6(10):782-92
PMID 16145555
 
Array comparative genomic hybridization and its applications in cancer
Pinkel D, Albertson DG
Nat Genet 2005 Jun;37 Suppl:S11-7
 
Genetic diagnosis in malignant hemopathies: from cytogenetics to next-generation sequencing
De Braekeleer E, Douet-Guilbert N, De Braekeleer M
Expert Rev Mol Diagn 2014 Mar;14(2):127-9
PMID 24437978
 
Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer
Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM
Science 2005 Oct 28;310(5748):644-8
PMID 16254181
 
A landscape effect in tenosynovial giant-cell tumor from activation of CSF1 expression by a translocation in a minority of tumor cells
West RB, Rubin BP, Miller MA, Subramanian S, Kaygusuz G, Montgomery K, Zhu S, Marinelli RJ, De Luca A, Downs-Kelly E, Goldblum JR, Corless CL, Brown PO, Gilks CB, Nielsen TO, Huntsman D, van de Rijn M
Proc Natl Acad Sci U S A 2006 Jan 17;103(3):690-5
PMID 16407111
 
Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer
Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, Nardone J, Lee K, Reeves C, Li Y, Hu Y, Tan Z, Stokes M, Sullivan L, Mitchell J, Wetzel R, Macneill J, Ren JM, Yuan J, Bakalarski CE, Villen J, Kornhauser JM, Smith B, Li D, Zhou X, Gygi SP, Gu TL, Polakiewicz RD, Rush J, Comb MJ
Cell 2007 Dec 14;131(6):1190-203
PMID 18083107
 
Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer
Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, Fujiwara S, Watanabe H, Kurashina K, Hatanaka H, Bando M, Ohno S, Ishikawa Y, Aburatani H, Niki T, Sohara Y, Sugiyama Y, Mano H
Nature 2007 Aug 2;448(7153):561-6
PMID 17625570
 
Identification of a novel, recurrent HEY1-NCOA2 fusion in mesenchymal chondrosarcoma based on a genome-wide screen of exon-level expression data
Wang L, Motoi T, Khanin R, Olshen A, Mertens F, Bridge J, Dal Cin P, Antonescu CR, Singer S, Hameed M, Bovee JV, Hogendoorn PC, Socci N, Ladanyi M
Genes Chromosomes Cancer 2012 Feb;51(2):127-39
PMID 22034177
 
Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing
Campbell PJ, Stephens PJ, Pleasance ED, O'Meara S, Li H, Santarius T, Stebbings LA, Leroy C, Edkins S, Hardy C, Teague JW, Menzies A, Goodhead I, Turner DJ, Clee CM, Quail MA, Cox A, Brown C, Durbin R, Hurles ME, Edwards PA, Bignell GR, Stratton MR, Futreal PA
Nat Genet 2008 Jun;40(6):722-9
PMID 18438408
 
Transcriptome sequencing to detect gene fusions in cancer
Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, Sam L, Barrette T, Palanisamy N, Chinnaiyan AM
Nature 2009 Mar 5;458(7234):97-101
PMID 19136943
 
Chimeric transcript discovery by paired-end transcriptome sequencing
Maher CA, Palanisamy N, Brenner JC, Cao X, Kalyana-Sundaram S, Luo S, Khrebtukova I, Barrette TR, Grasso C, Yu J, Lonigro RJ, Schroth G, Kumar-Sinha C, Chinnaiyan AM
Proc Natl Acad Sci U S A 2009 Jul 28;106(30):12353-8
PMID 19592507
 
Complex landscapes of somatic rearrangement in human breast cancer genomes
Stephens PJ, McBride DJ, Lin ML, Varela I, Pleasance ED, Simpson JT, Stebbings LA, Leroy C, Edkins S, Mudie LJ, Greenman CD, Jia M, Latimer C, Teague JW, Lau KW, Burton J, Quail MA, Swerdlow H, Churcher C, Natrajan R, Sieuwerts AM, Martens JW, Silver DP, Langerød A, Russnes HE, Foekens JA, Reis-Filho JS, van 't Veer L, Richardson AL, Børresen-Dale AL, Campbell PJ, Futreal PA, Stratton MR
Nature 2009 Dec 24;462(7276):1005-10
PMID 20033038
 
Comprehensive molecular characterization of clear cell renal cell carcinoma
Cancer Genome Atlas Research Network
Nature 2013 Jul 4;499(7456):43-9
PMID 23792563
 
Comprehensive genomic characterization of squamous cell lung cancers
Cancer Genome Atlas Research Network
Nature 2012 Sep 27;489(7417):519-25
PMID 22960745
 
Comprehensive molecular characterization of urothelial bladder carcinoma
Cancer Genome Atlas Research Network
Nature 2014 Mar 20;507(7492):315-22
PMID 24476821
 
Integrated genomic characterization of endometrial carcinoma
Cancer Genome Atlas Research Network, Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C, Laird PW, Ding L, Zhang W, Mills GB, Kucherlapati R, Mardis ER, Levine DA
Nature 2013 May 2;497(7447):67-73
PMID 23636398
 
MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers
Steidl C, Shah SP, Woolcock BW, Rui L, Kawahara M, Farinha P, Johnson NA, Zhao Y, Telenius A, Neriah SB, McPherson A, Meissner B, Okoye UC, Diepstra A, van den Berg A, Sun M, Leung G, Jones SJ, Connors JM, Huntsman DG, Savage KJ, Rimsza LM, Horsman DE, Staudt LM, Steidl U, Marra MA, Gascoyne RD
Nature 2011 Mar 17;471(7338):377-81
PMID 21368758
 
Use of whole-genome sequencing to diagnose a cryptic fusion oncogene
Welch JS, Westervelt P, Ding L, Larson DE, Klco JM, Kulkarni S, Wallis J, Chen K, Payton JE, Fulton RS, Veizer J, Schmidt H, Vickery TL, Heath S, Watson MA, Tomasson MH, Link DC, Graubert TA, DiPersio JF, Mardis ER, Ley TJ, Wilson RK
JAMA 2011 Apr 20;305(15):1577-84
PMID 21505136
 
Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia
Roberts KG, Morin RD, Zhang J, Hirst M, Zhao Y, Su X, Chen SC, Payne-Turner D, Churchman ML, Harvey RC, Chen X, Kasap C, Yan C, Becksfort J, Finney RP, Teachey DT, Maude SL, Tse K, Moore R, Jones S, Mungall K, Birol I, Edmonson MN, Hu Y, Buetow KE, Chen IM, Carroll WL, Wei L, Ma J, Kleppe M, Levine RL, Garcia-Manero G, Larsen E, Shah NP, Devidas M, Reaman G, Smith M, Paugh SW, Evans WE, Grupp SA, Jeha S, Pui CH, Gerhard DS, Downing JR, Willman CL, Loh M, Hunger SP, Marra MA, Mullighan CG
Cancer Cell 2012 Aug 14;22(2):153-66
PMID 22897847
 
The landscape and therapeutic relevance of cancer-associated transcript fusions
Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, Verhaak RG
Oncogene 2015 Sep 10;34(37):4845-54
PMID 25500544
 
Mitelman database of chromosome aberrations and genes fusions in Cancer
Mitelman F, Johansson B, Merten sF
Mitelman F, Johansson B and Mertens F (Eds.) 2016, http://cgap.nci.nih.gov/Chromosomes/Mitelman
 
Atlas of genetics and cytogenetics in oncology and haematology in 2013
Huret JL, Ahmad M, Arsaban M, Bernheim A, Cigna J, Desangles F, Guignard JC, Jacquemot-Perbal MC, Labarussias M, Leberre V, Malo A, Morel-Pair C, Mossafa H, Potier JC, Texier G, Viguié F, Yau Chun Wan-Senon S, Zasadzinski A, Dessen P
Nucleic Acids Res 2013 Jan;41(Database issue):D920-4
PMID 23161685
 
The impact of translocations and gene fusions on cancer causation
Mitelman F, Johansson B, Mertens F
Nat Rev Cancer 2007 Apr;7(4):233-45
PMID 17361217
 
Gene fusions associated with recurrent amplicons represent a class of passenger aberrations in breast cancer
Kalyana-Sundaram S, Shankar S, Deroo S, Iyer MK, Palanisamy N, Chinnaiyan AM, Kumar-Sinha C
Neoplasia 2012 Aug;14(8):702-8
PMID 22952423
 
Implications of chimaeric non-co-linear transcripts
Gingeras TR
Nature 2009 Sep 10;461(7261):206-11
PMID 19741701
 
SLC45A3-ELK4 is a novel and frequent erythroblast transformation-specific fusion transcript in prostate cancer
Rickman DS, Pflueger D, Moss B, VanDoren VE, Chen CX, de la Taille A, Kuefer R, Tewari AK, Setlur SR, Demichelis F, Rubin MA
Cancer Res 2009 Apr 1;69(7):2734-8
PMID 19293179
 
New insights to the MLL recombinome of acute leukemias
Meyer C, Kowarz E, Hofmann J, Renneville A, Zuna J, Trka J, Ben Abdelali R, Macintyre E, De Braekeleer E, De Braekeleer M, Delabesse E, de Oliveira MP, Cavé H, Clappier E, van Dongen JJ, Balgobind BV, van den Heuvel-Eibrink MM, Beverloo HB, Panzer-Grümayer R, Teigler-Schlegel A, Harbott J, Kjeldsen E, Schnittger S, Koehl U, Gruhn B, Heidenreich O, Chan LC, Yip SF, Krzywinski M, Eckert C, Möricke A, Schrappe M, Alonso CN, Schäfer BW, Krauter J, Lee DA, Zur Stadt U, Te Kronnie G, Sutton R, Izraeli S, Trakhtenbrot L, Lo Nigro L, Tsaur G, Fechina L, Szczepanski T, Strehl S, Ilencikova D, Molkentin M, Burmeister T, Dingermann T, Klingebiel T, Marschalek R
Leukemia 2009 Aug;23(8):1490-9
PMID 19262598
 
Next-generation sequencing of RNA and DNA isolated from paired fresh-frozen and formalin-fixed paraffin-embedded samples of human cancer and normal tissue
Hedegaard J, Thorsen K, Lund MK, Hein AM, Hamilton-Dutoit SJ, Vang S, Nordentoft I, Birkenkamp-Demtröder K, Kruhøffer M, Hager H, Knudsen B, Andersen CL, Sørensen KD, Pedersen JS, Ørntoft TF, Dyrskjøt L
PLoS One 2014 May 30;9(5):e98187
PMID 24878701
 
The evolving classification of soft tissue tumours - an update based on the new 2013 WHO classification
Fletcher CD
Histopathology 2014 Jan;64(1):2-11
PMID 24164390
 
The 2016 revision of the World Health Organization (WHO) classification of lymphoid neoplasms
Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, Advani R, Ghielmini M, Salles GA, Zelenetz AD, Jaffe ES
Blood 2016 Mar 15
PMID 26980727
 
Towards individualized follow-up in adult acute myeloid leukemia in remission
Hokland P, Ommen HB
Blood 2011 Mar 3;117(9):2577-84
PMID 21097673
 
Liquid biopsy: monitoring cancer-genetics in the blood
Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A
Nat Rev Clin Oncol 2013 Aug;10(8):472-84
PMID 23836314
 
Microfluidic, marker-free isolation of circulating tumor cells from blood samples
Karabacak NM, Spuhler PS, Fachin F, Lim EJ, Pai V, Ozkumur E, Martel JM, Kojic N, Smith K, Chen PI, Yang J, Hwang H, Morgan B, Trautwein J, Barber TA, Stott SL, Maheswaran S, Kapur R, Haber DA, Toner M
Nat Protoc 2014 Mar;9(3):694-710
PMID 24577360
 
A novel flow cytometry-based cell capture platform for the detection, capture and molecular characterization of rare tumor cells in blood
Watanabe M, Serizawa M, Sawada T, Takeda K, Takahashi T, Yamamoto N, Koizumi F, Koh Y
J Transl Med 2014 May 23;12:143
PMID 24886394
 
Pharmacogenomic modeling of circulating tumor and invasive cells for prediction of chemotherapy response and resistance in pancreatic cancer
Yu KH, Ricigliano M, Hidalgo M, Abou-Alfa GK, Lowery MA, Saltz LB, Crotty JF, Gary K, Cooper B, Lapidus R, Sadowska M, O'Reilly EM
Clin Cancer Res 2014 Oct 15;20(20):5281-9
PMID 25107917
 
Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay
Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V, Klein C, Saini M, Bäuerle T, Wallwiener M, Holland-Letz T, Höfner T, Sprick M, Scharpff M, Marmé F, Sinn HP, Pantel K, Weichert W, Trumpp A
Nat Biotechnol 2013 Jun;31(6):539-44
PMID 23609047
 
Development of personalized tumor biomarkers using massively parallel sequencing
Leary RJ, Kinde I, Diehl F, Schmidt K, Clouser C, Duncan C, Antipova A, Lee C, McKernan K, De La Vega FM, Kinzler KW, Vogelstein B, Diaz LA Jr, Velculescu VE
Sci Transl Med 2010 Feb 24;2(20):20ra14
PMID 20371490
 
Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome
Druker BJ, Sawyers CL, Kantarjian H, Resta DJ, Reese SF, Ford JM, Capdeville R, Talpaz M
N Engl J Med 2001 Apr 5;344(14):1038-42
PMID 11287973
 
Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia
Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM, Lydon NB, Kantarjian H, Capdeville R, Ohno-Jones S, Sawyers CL
N Engl J Med 2001 Apr 5;344(14):1031-7
PMID 11287972
 
Imatinib mesylate in advanced dermatofibrosarcoma protuberans: pooled analysis of two phase II clinical trials
Rutkowski P, Van Glabbeke M, Rankin CJ, Ruka W, Rubin BP, Debiec-Rychter M, Lazar A, Gelderblom H, Sciot R, Lopez-Terrada D, Hohenberger P, van Oosterom AT, Schuetze SM; European Organisation for Research and Treatment of Cancer Soft Tissue/Bone Sarcoma Group; Southwest Oncology Group
J Clin Oncol 2010 Apr 1;28(10):1772-9
PMID 20194851
 
Adjuvant treatment of GIST: patient selection and treatment strategies
Joensuu H
Nat Rev Clin Oncol 2012 Apr 24;9(6):351-8
PMID 22525709
 
Philadelphia chromosome-positive acute lymphoblastic leukemia: current treatment and future perspectives
Lee HJ, Thompson JE, Wang ES, Wetzler M
Cancer 2011 Apr 15;117(8):1583-94
PMID 21472706
 
RET fusion gene: translation to personalized lung cancer therapy
Kohno T, Tsuta K, Tsuchihara K, Nakaoku T, Yoh K, Goto K
Cancer Sci 2013 Nov;104(11):1396-400
PMID 23991695
 
Tyrosine kinase gene rearrangements in epithelial malignancies
Shaw AT, Hsu PP, Awad MM, Engelman JA
Nat Rev Cancer 2013 Nov;13(11):772-87
PMID 24132104
 
Targeting the MLL complex in castration-resistant prostate cancer
Malik R, Khan AP, Asangani IA, Cielik M, Prensner JR, Wang X, Iyer MK, Jiang X, Borkin D, Escara-Wilke J, Stender R, Wu YM, Niknafs YS, Jing X, Qiao Y, Palanisamy N, Kunju LP, Krishnamurthy PM, Yocum AK, Mellacheruvu D, Nesvizhskii AI, Cao X, Dhanasekaran SM, Feng FY, Grembecka J, Cierpicki T, Chinnaiyan AM
Nat Med 2015 Apr;21(4):344-52
PMID 25822367
 
DOT1L inhibits SIRT1-mediated epigenetic silencing to maintain leukemic gene expression in MLL-rearranged leukemia
Chen CW, Koche RP, Sinha AU, Deshpande AJ, Zhu N, Eng R, Doench JG, Xu H, Chu SH, Qi J, Wang X, Delaney C, Bernt KM, Root DE, Hahn WC, Bradner JE, Armstrong SA
Nat Med 2015 Apr;21(4):335-43
PMID 25822366
 
Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia
Dawson MA, Prinjha RK, Dittmann A, Giotopoulos G, Bantscheff M, Chan WI, Robson SC, Chung CW, Hopf C, Savitski MM, Huthmacher C, Gudgin E, Lugo D, Beinke S, Chapman TD, Roberts EJ, Soden PE, Auger KR, Mirguet O, Doehner K, Delwel R, Burnett AK, Jeffrey P, Drewes G, Lee K, Huntly BJ, Kouzarides T
Nature 2011 Oct 2;478(7370):529-33
PMID 21964340
 
EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations
McCabe MT, Ott HM, Ganji G, Korenchuk S, Thompson C, Van Aller GS, Liu Y, Graves AP, Della Pietra A 3rd, Diaz E, LaFrance LV, Mellinger M, Duquenne C, Tian X, Kruger RG, McHugh CF, Brandt M, Miller WH, Dhanak D, Verma SK, Tummino PJ, Creasy CL
Nature 2012 Dec 6;492(7427):108-12
PMID 23051747
 
EZH2 inhibition sensitizes BRG1 and EGFR mutant lung tumours to TopoII inhibitors
Fillmore CM, Xu C, Desai PT, Berry JM, Rowbotham SP, Lin YJ, Zhang H, Marquez VE, Hammerman PS, Wong KK, Kim CF
Nature 2015 Apr 9;520(7546):239-42
PMID 25629630
 
[Cytogenetics, cytogenomics and cancer: 2004 update]
Bernheim A, Huret JL, Guillaud-Bataille M, Brison O, Couturiers J; Groupe Français de Cytogéné Oncologique
Bull Cancer 2004 Jan;91(1):29-43
PMID 14975803
 
Genetics and metabolism in Neurospora
BEADLE GW
Physiol Rev 1945 Oct;25:643-63
PMID 21004451
 
The GenBank nucleic acid sequence database
Burks C, Fickett JW, Goad WB, Kanehisa M, Lewitter FI, Rindone WP, Swindell CD, Tung CS, Bilofsky HS
Comput Appl Biosci 1985 Dec;1(4):225-33
PMID 3880345
 
GenBank
Burks C, Cassidy M, Cinkosky MJ, Cumella KE, Gilna P, Hayden JE, Keen GM, Kelley TA, Kelly M, Kristofferson D, et al
Nucleic Acids Res 1991 Apr 25;19 Suppl:2221-5
PMID 2041806
 
Recent changes in the GenBank On-line Service
Benton D
Nucleic Acids Res 1990 Mar 25;18(6):1517-20
PMID 2326192
 
GenBank
Benson DA, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW
Nucleic Acids Res 2015 Jan;43(Database issue):D30-5
PMID 25414350
 
The European Bioinformatics Institute in 2016: Data growth and integration
Cook CE, Bergman MT, Finn RD, Cochrane G, Birney E, Apweiler R
Nucleic Acids Res 2016 Jan 4;44(D1):D20-6
PMID 26673705
 
Searching and Navigating UniProt Databases
Pundir S, Magrane M, Martin MJ, O'Donovan C; UniProt Consortium
Curr Protoc Bioinformatics 2015 Jun 19;50:1
PMID 26088053
 
Genenames.org
Gray KA, Yates B, Seal RL, Wright MW, Bruford EA
the HGNC resources in 2015 Nucleic Acids Res
PMID 25361968
 
Database resources of the National Center for Biotechnology Information
NCBI Resource Coordinators
Nucleic Acids Res 2016 Jan 4;44(D1):D7-19
PMID 26615191
 
Genic insights from integrated human proteomics in GeneCards
Fishilevich S, Zimmerman S, Kohn A, Iny Stein T, Olender T, Kolker E, Safran M, Lancet D
Database (Oxford) 2016 Apr 5;2016
PMID 27048349
 
The UCSC Cancer Genomics Browser: update 2015
Goldman M, Craft B, Swatloski T, Cline M, Morozova O, Diekhans M, Haussler D, Zhu J
Nucleic Acids Res 2015 Jan;43(Database issue):D812-7
PMID 25392408
 
Ensembl 2016
Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D, Cummins C, Clapham P, Fitzgerald S, Gil L, Girón CG, Gordon L, Hourlier T, Hunt SE, Janacek SH, Johnson N, Juettemann T, Keenan S, Lavidas I, Martin FJ, Maurel T, McLaren W, Murphy DN, Nag R, Nuhn M, Parker A, Patricio M, Pignatelli M, Rahtz M, Riat HS, Sheppard D, Taylor K, Thormann A, Vullo A, Wilder SP, Zadissa A, Birney E, Harrow J, Muffato M, Perry E, Ruffier M, Spudich G, Trevanion SJ, Cunningham F, Aken BL, Zerbino DR, Flicek P
Nucleic Acids Res 2016 Jan 4;44(D1):D710-6
PMID 26687719
 
Integrated genomic analyses of ovarian carcinoma
Cancer Genome Atlas Research Network
Nature 2011 Jun 29;474(7353):609-15
PMID 21720365
 
International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data
Zhang J, Baran J, Cros A, Guberman JM, Haider S, Hsu J, Liang Y, Rivkin E, Wang J, Whitty B, Wong-Erasmus M, Yao L, Kasprzyk A
Database (Oxford) 2011 Sep 19;2011:bar026
PMID 21930502
 
Making sense of cancer genomic data.
Chin L, Hahn WC, Getz G, Meyerson M.
Genes Dev. 2011 Mar 15;25(6):534-55. doi: 10.1101/gad.2017311.
PMID 21406553
 
Human cancer databases (review)
Pavlopoulou A, Spandidos DA, Michalopoulos I
Oncol Rep 2015 Jan;33(1):3-18
PMID 25369839
 
Oncogenomic portals for the visualization and analysis of genome-wide cancer data
Klonowska K, Czubak K, Wojciechowska M, Handschuh L, Zmienko A, Figlerowicz M, Dams-Kozlowska H, Kozlowski P
Oncotarget 2016 Jan 5;7(1):176-92
PMID 26484415
 
Human genotype-phenotype databases: aims, challenges and opportunities
Brookes AJ, Robinson PN
Nat Rev Genet 2015 Dec;16(12):702-15
PMID 26553330
 
Databases and web tools for cancer genomics study
Yang Y, Dong X, Xie B, Ding N, Chen J, Li Y, Zhang Q, Qu H, Fang X
Genomics Proteomics Bioinformatics 2015 Feb;13(1):46-50
PMID 25707591
 
Variation Interpretation Predictors: Principles, Types, Performance, and Choice
Niroula A, Vihinen M
Hum Mutat 2016 Jun;37(6):579-97
PMID 26987456
 
Deciphering ENCODE
Diehl AG, Boyle AP
Trends Genet 2016 Apr;32(4):238-49
PMID 26962025
 
dbWGFP: a database and web server of human whole-genome single nucleotide variants and their functional predictions
Wu J, Wu M, Li L, Liu Z, Zeng W, Jiang R
Database (Oxford) 2016 Mar 17;2016
PMID 26989155
 
Somatic mutation in cancer and normal cells
Martincorena I, Campbell PJ
Science 2015 Sep 25;349(6255):1483-9
PMID 26404825
 
Cancer Cytogenetics: Chromosomal and Molecular Genetic Abberations of Tumor Cells
Sverre Heim and Felix Mitelman
2015, Wiley-Blackwell , New-York
 
A database on cytogenetics in haematology and oncology
Dorkeld F, Bernheim A, Dessen P, Huret JL
Nucleic Acids Res 1999 Jan 1;27(1):353-4
PMID 9847226
 
Lancet
A PROPOSED standard system of nomenclature of human mitotic chromosomes
1960 May 14;1(7133):1063-5 PubMed PMID: 13857542
PMID 13857542
 
An International System for Human Cytogenetic Nomenclature
Shaffer LG, McGowen-Jordan J, Schmid M, editors
2013, Basel: S. Karger
 
Mitelman database of chromosome aberrations and genes fusions in Cancer
Mitelman F, Johansson B, Mertens F
 
ChimerDB--a knowledgebase for fusion sequences
Kim N, Kim P, Nam S, Shin S, Lee S
Nucleic Acids Res 2006 Jan 1;34(Database issue):D21-4
PMID 16381848
 
ChimerDB 2
Kim P, Yoon S, Kim N, Lee S, Ko M, Lee H, Kang H, Kim J, Lee S
0--a knowledgebase for fusion genes updated Nucleic Acids Res
PMID 19906715
 
TICdb: a collection of gene-mapped translocation breakpoints in cancer
Novo FJ, de Mendíbil IO, Vizmanos JL
BMC Genomics 2007 Jan 26;8:33
PMID 17257420
 
OMIM
Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A
org: Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic disorders Nucleic Acids Res
PMID 25428349
 
COSMIC: exploring the world's knowledge of somatic mutations in human cancer
Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S, Kok CY, Jia M, De T, Teague JW, Stratton MR, McDermott U, Campbell PJ
Nucleic Acids Res 2015 Jan;43(Database issue):D805-11
PMID 25355519
 
ChiTaRS: a database of human, mouse and fruit fly chimeric transcripts and RNA-sequencing data
Frenkel-Morgenstern M, Gorohovski A, Lacroix V, Rogers M, Ibanez K, Boullosa C, Andres Leon E, Ben-Hur A, Valencia A
Nucleic Acids Res 2013 Jan;41(Database issue):D142-51
PMID 23143107
 
ChiTaRS 2
Frenkel-Morgenstern M, Gorohovski A, Vucenovic D, Maestre L, Valencia A
1--an improved database of the chimeric transcripts and RNA-seq data with novel sense-antisense chimeric RNA transcripts Nucleic Acids Res
PMID 25414346
 
A comprehensive transcriptional portrait of human cancer cell lines
Klijn C, Durinck S, Stawiski EW, Haverty PM, Jiang Z, Liu H, Degenhardt J, Mayba O, Gnad F, Liu J, Pau G, Reeder J, Cao Y, Mukhyala K, Selvaraj SK, Yu M, Zynda GJ, Brauer MJ, Wu TD, Gentleman RC, Manning G, Yauch RL, Bourgon R, Stokoe D, Modrusan Z, Neve RM, de Sauvage FJ, Settleman J, Seshagiri S, Zhang Z
Nat Biotechnol 2015 Mar;33(3):306-12
PMID 25485619
 
FusionCancer: a database of cancer fusion genes derived from RNA-seq data
Wang Y, Wu N, Liu J, Wu Z, Dong D
Diagn Pathol 2015 Jul 28;10:131
PMID 26215638
 
Fusion gene microarray reveals cancer type-specificity among fusion genes
Løvf M, Thomassen GO, Bakken AC, Celestino R, Fioretos T, Lind GE, Lothe RA, Skotheim RI
Genes Chromosomes Cancer 2011 May;50(5):348-57
PMID 21305644
 
A universal assay for detection of oncogenic fusion transcripts by oligo microarray analysis
Skotheim RI, Thomassen GO, Eken M, Lind GE, Micci F, Ribeiro FR, Cerveira N, Teixeira MR, Heim S, Rognes T, Lothe RA
Mol Cancer 2009 Jan 19;8:5
PMID 19152679
 
Next generation sequencing approach for detecting 491 fusion genes from human cancer
Urakami K, Shimoda Y, Ohshima K, Nagashima T, Serizawa M, Tanabe T, Saito J, Usui T, Watanabe Y, Naruoka A, Ohnami S, Ohnami S, Mochizuki T, Kusuhara M, Yamaguchi K
Biomed Res 2016;37(1):51-62
PMID 26912140
 
Recurrent chimeric fusion RNAs in non-cancer tissues and cells
Babiceanu M, Qin F, Xie Z, Jia Y, Lopez K, Janus N, Facemire L, Kumar S, Pang Y, Qi Y, Lazar IM, Li H
Nucleic Acids Res 2016 Apr 7;44(6):2859-72
PMID 26837576
 
Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors
Kallioniemi A, Kallioniemi OP, Sudar D, Rutovitz D, Gray JW, Waldman F, Pinkel D
Science 1992 Oct 30;258(5083):818-21
PMID 1359641
 
Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances
Solinas-Toldo S, Lampel S, Stilgenbauer S, Nickolenko J, Benner A, Döhner H, Cremer T, Lichter P
Genes Chromosomes Cancer 1997 Dec;20(4):399-407
PMID 9408757
 
High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays
Pinkel D, Segraves R, Sudar D, Clark S, Poole I, Kowbel D, Collins C, Kuo WL, Chen C, Zhai Y, Dairkee SH, Ljung BM, Gray JW, Albertson DG
Nat Genet 1998 Oct;20(2):207-11
PMID 9771718
 
Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology
Commo F, Ferté C, Soria JC, Friend SH, André F, Guinney J
Ann Oncol 2015 Mar;26(3):582-8
PMID 25538175
 
The Gene Expression Omnibus Database
Clough E, Barrett T
Methods Mol Biol 2016;1418:93-110
PMID 27008011
 
Expression Atlas update--an integrated database of gene and protein expression in humans, animals and plants
Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E, Burdett T, Füllgrabe A, Fuentes AM, Jupp S, Koskinen S, Mannion O, Huerta L, Megy K, Snow C, Williams E, Barzine M, Hastings E, Weisser H, Wright J, Jaiswal P, Huber W, Choudhary J, Parkinson HE, Brazma A
Nucleic Acids Res 2016 Jan 4;44(D1):D746-52
PMID 26481351
 
The landscape of somatic copy-number alteration across human cancers
Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, Mc Henry KT, Pinchback RM, Ligon AH, Cho YJ, Haery L, Greulich H, Reich M, Winckler W, Lawrence MS, Weir BA, Tanaka KE, Chiang DY, Bass AJ, Loo A, Hoffman C, Prensner J, Liefeld T, Gao Q, Yecies D, Signoretti S, Maher E, Kaye FJ, Sasaki H, Tepper JE, Fletcher JA, Tabernero J, Baselga J, Tsao MS, Demichelis F, Rubin MA, Janne PA, Daly MJ, Nucera C, Levine RL, Ebert BL, Gabriel S, Rustgi AK, Antonescu CR, Ladanyi M, Letai A, Garraway LA, Loda M, Beer DG, True LD, Okamoto A, Pomeroy SL, Singer S, Golub TR, Lander ES, Getz G, Sellers WR, Meyerson M
Nature 2010 Feb 18;463(7283):899-905
PMID 20164920
 
Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes
Kim TM, Xi R, Luquette LJ, Park RW, Johnson MD, Park PJ
Genome Res 2013 Feb;23(2):217-27
PMID 23132910
 
CaSNP: a database for interrogating copy number alterations of cancer genome from SNP array data
Cao Q, Zhou M, Wang X, Meyer CA, Zhang Y, Chen Z, Li C, Liu XS
Nucleic Acids Res 2011 Jan;39(Database issue):D968-74
PMID 20972221
 
arrayMap 2014: an updated cancer genome resource
Cai H, Gupta S, Rath P, Ai N, Baudis M
Nucleic Acids Res 2015 Jan;43(Database issue):D825-30
PMID 25428357
 
Detection of large-scale variation in the human genome
Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C
Nat Genet 2004 Sep;36(9):949-51
PMID 15286789
 
Global variation in copy number in the human genome
Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W, Cho EK, Dallaire S, Freeman JL, González JR, Gratacòs M, Huang J, Kalaitzopoulos D, Komura D, MacDonald JR, Marshall CR, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville MJ, Tchinda J, Valsesia A, Woodwark C, Yang F, Zhang J, Zerjal T, Zhang J, Armengol L, Conrad DF, Estivill X, Tyler-Smith C, Carter NP, Aburatani H, Lee C, Jones KW, Scherer SW, Hurles ME
Nature 2006 Nov 23;444(7118):444-54
PMID 17122850
 
The Database of Genomic Variants: a curated collection of structural variation in the human genome
MacDonald JR, Ziman R, Yuen RK, Feuk L, Scherer SW
Nucleic Acids Res 2014 Jan;42(Database issue):D986-92
PMID 24174537
 
DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources
Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, Van Vooren S, Moreau Y, Pettett RM, Carter NP
Am J Hum Genet 2009 Apr;84(4):524-33
 
A global reference for human genetic variation
1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR
Nature 2015 Oct 1;526(7571):68-74
PMID 26432245
 
Integrating common and rare genetic variation in diverse human populations
International HapMap 3 Consortium, Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF, Yu F, Peltonen L, Dermitzakis E, Bonnen PE, Altshuler DM, Gibbs RA, de Bakker PI, Deloukas P, Gabriel SB, Gwilliam R, Hunt S, Inouye M, Jia X, Palotie A, Parkin M, Whittaker P, Yu F, Chang K, Hawes A, Lewis LR, Ren Y, Wheeler D, Gibbs RA, Muzny DM, Barnes C, Darvishi K, Hurles M, Korn JM, Kristiansson K, Lee C, McCarrol SA, Nemesh J, Dermitzakis E, Keinan A, Montgomery SB, Pollack S, Price AL, Soranzo N, Bonnen PE, Gibbs RA, Gonzaga-Jauregui C, Keinan A, Price AL, Yu F, Anttila V, Brodeur W, Daly MJ, Leslie S, McVean G, Moutsianas L, Nguyen H, Schaffner SF, Zhang Q, Ghori MJ, McGinnis R, McLaren W, Pollack S, Price AL, Schaffner SF, Takeuchi F, Grossman SR, Shlyakhter I, Hostetter EB, Sabeti PC, Adebamowo CA, Foster MW, Gordon DR, Licinio J, Manca MC, Marshall PA, Matsuda I, Ngare D, Wang VO, Reddy D, Rotimi CN, Royal CD, Sharp RR, Zeng C, Brooks LD, McEwen JE
Nature 2010 Sep 2;467(7311):52-8
PMID 20811451
 
Evolution and functional impact of rare coding variation from deep sequencing of human exomes
Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, Kang HM, Jordan D, Leal SM, Gabriel S, Rieder MJ, Abecasis G, Altshuler D, Nickerson DA, Boerwinkle E, Sunyaev S, Bustamante CD, Bamshad MJ, Akey JM; Broad GO; Seattle GO; NHLBI Exome Sequencing Project
Science 2012 Jul 6;337(6090):64-9
PMID 22604720
 
A census of human cancer genes
Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR
Nat Rev Cancer 2004 Mar;4(3):177-83
PMID 14993899
 
Human Gene Mutation Database
Cooper DN, Krawczak M
Hum Genet 1996 Nov;98(5):629
PMID 8882888
 
LOVD v. 2.0: the next generation in gene variant databases.
Fokkema IF, Taschner PE, Schaafsma GC, Celli J, Laros JF, den Dunnen JT
Hum Mutat. May;32(5):557-63
PMID 21520333
 
Web-TCGA: an online platform for integrated analysis of molecular cancer data sets
Deng M, Brägelmann J, Schultze JL, Perner S
BMC Bioinformatics 2016 Feb 6;17:72
PMID 26852330
 
IntOGen: integration and data mining of multidimensional oncogenomic data
Gundem G, Perez-Llamas C, Jene-Sanz A, Kedzierska A, Islam A, Deu-Pons J, Furney SJ, Lopez-Bigas N
Nat Methods 2010 Feb;7(2):92-3
PMID 20111033
 
A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)
Wu TJ, Shamsaddini A, Pan Y, Smith K, Crichton DJ, Simonyan V, Mazumder R
Database (Oxford) 2014 Mar 25;2014:bau022
PMID 24667251
 
Quantifying prion disease penetrance using large population control cohorts
Minikel EV, Vallabh SM, Lek M, Estrada K, Samocha KE, Sathirapongsasuti JF, McLean CY, Tung JY, Yu LP, Gambetti P, Blevins J, Zhang S, Cohen Y, Chen W, Yamada M, Hamaguchi T, Sanjo N, Mizusawa H, Nakamura Y, Kitamoto T, Collins SJ, Boyd A, Will RG, Knight R, Ponto C, Zerr I, Kraus TF, Eigenbrod S, Giese A, Calero M, de Pedro-Cuesta J, Haïk S, Laplanche JL, Bouaziz-Amar E, Brandel JP, Capellari S, Parchi P, Poleggi A, Ladogana A, O'Donnell-Luria AH, Karczewski KJ, Marshall JL, Boehnke M, Laakso M, Mohlke KL, Kähler A, Chambert K, McCarroll S, Sullivan PF, Hultman CM, Purcell SM, Sklar P, van der Lee SJ, Rozemuller A, Jansen C, Hofman A, Kraaij R, van Rooij JG, Ikram MA, Uitterlinden AG, van Duijn CM; Exome Aggregation Consortium (ExAC), Daly MJ, MacArthur DG
Sci Transl Med 2016 Jan 20;8(322):322ra9
PMID 26791950
 
Birth Defects
Cytogenet Cell Genet. 1974;13(3):1-216
 
Using ClinVar as a Resource to Support Variant Interpretation
Harrison SM, Riggs ER, Maglott DR, Lee JM, Azzariti DR, Niehaus A, Ramos EM, Martin CL, Landrum MJ, Rehm HL
Curr Protoc Hum Genet 2016 Apr 1;89:8
PMID 27037489
 
Identification and analysis of deleterious human SNPs
Yue P, Moult J
J Mol Biol 2006 Mar 10;356(5):1263-74
PMID 16412461
 
The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency
Rubinstein WS, Maglott DR, Lee JM, Kattman BL, Malheiro AJ, Ovetsky M, Hem V, Gorelenkov V, Song G, Wallin C, Husain N, Chitipiralla S, Katz KS, Hoffman D, Jang W, Johnson M, Karmanov F, Ukrainchik A, Denisenko M, Fomous C, Hudson K, Ostell JM
Nucleic Acids Res 2013 Jan;41(Database issue):D925-35
PMID 23193275
 
Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation
O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O'Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD
Nucleic Acids Res 2016 Jan 4;44(D1):D733-45
PMID 26553804
 
The UCSC Genome Browser database: 2015 update
Rosenbloom KR, Armstrong J, Barber GP, Casper J, Clawson H, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M, Harte RA, Heitner S, Hickey G, Hinrichs AS, Hubley R, Karolchik D, Learned K, Lee BT, Li CH, Miga KH, Nguyen N, Paten B, Raney BJ, Smit AF, Speir ML, Zweig AS, Haussler D, Kuhn RM, Kent WJ
Nucleic Acids Res 2015 Jan;43(Database issue):D670-81
PMID 25428374
 
BioGPS: building your own mash-up of gene annotations and expression profiles
Wu C, Jin X, Tsueng G, Afrasiabi C, Su AI
Nucleic Acids Res 2016 Jan 4;44(D1):D313-6
PMID 26578587
 
UniProt: a hub for protein information
UniProt Consortium
Nucleic Acids Res 2015 Jan;43(Database issue):D204-12
PMID 25348405
 
The neXtProt knowledgebase on human proteins: current status
Gaudet P, Michel PA, Zahn-Zabal M, Cusin I, Duek PD, Evalet O, Gateau A, Gleizes A, Pereira M, Teixeira D, Zhang Y, Lane L, Bairoch A
Nucleic Acids Res 2015 Jan;43(Database issue):D764-70
PMID 25593349
 
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E
Nucleic Acids Res 2015 Jan;43(Database issue):D512-20
PMID 25514926
 
New and continuing developments at PROSITE
Sigrist CJ, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, Bougueleret L, Xenarios I
Nucleic Acids Res 2013 Jan;41(Database issue):D344-7
PMID 23161676
 
The Pfam protein families database: towards a more sustainable future
Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A
Nucleic Acids Res 2016 Jan 4;44(D1):D279-85
PMID 26673716
 
The InterPro protein families database: the classification resource after 15 years
Mitchell A, Chang HY, Daugherty L, Fraser M, Hunter S, Lopez R, McAnulla C, McMenamin C, Nuka G, Pesseat S, Sangrador-Vegas A, Scheremetjew M, Rato C, Yong SY, Bateman A, Punta M, Attwood TK, Sigrist CJ, Redaschi N, Rivoire C, Xenarios I, Kahn D, Guyot D, Bork P, Letunic I, Gough J, Oates M, Haft D, Huang H, Natale DA, Wu CH, Orengo C, Sillitoe I, Mi H, Thomas PD, Finn RD
Nucleic Acids Res 2015 Jan;43(Database issue):D213-21
PMID 25428371
 
GeneTests: an online genetic information resource for health care providers
Pagon RA
J Med Libr Assoc 2006 Jul;94(3):343-8
PMID 16888670
 
Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users
Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S
Hum Mutat 2012 May;33(5):803-8
PMID 22422702
 
Activation of proto-oncogenes by disruption of chromosome neighborhoods
Hnisz D, Weintraub AS, Day DS, Valton AL, Bak RO, Li CH, Goldmann J, Lajoie BR, Fan ZP, Sigova AA, Reddy J, Borges-Rivera D, Lee TI, Jaenisch R, Porteus MH, Dekker J, Young RA
Science 2016 Mar 25;351(6280):1454-8
PMID 26940867
 
Discovery of unfixed endogenous retrovirus insertions in diverse human populations
Wildschutte JH, Williams ZH, Montesion M, Subramanian RP, Kidd JM, Coffin JM
Proc Natl Acad Sci U S A 2016 Apr 19;113(16):E2326-34
PMID 27001843
 
All the World's a Stage: Facilitating Discovery Science and Improved Cancer Care through the Global Alliance for Genomics and Health
Lawler M, Siu LL, Rehm HL, Chanock SJ, Alterovitz G, Burn J, Calvo F, Lacombe D, Teh BT, North KN, Sawyers CL; Clinical Working Group of the Global Alliance for Genomics and Health (GA4GH)
Cancer Discov 2015 Nov;5(11):1133-6
PMID 26526696
 
Written2016-04Etienne De Braekeleer, Jean Loup Huret, Hossain Mossafa, Katriina Hautaviita, Philippe Dessen
Cancer Genetics & Stem Cell Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, United Kingdom; Medical Genetics, Dept Medical Information, University Hospital, F-86021 Poitiers, France; Laboratoire CERBA, 95310 Saint Ouen l'Aumone, France; (Mouse genomics, Wellcome Trust Sanger Institute); UMR 1170 INSERM, Gustave Roussy, 114 rue Edouard Vaillant, F-94805 Villejuif, France.

Citation

This paper should be referenced as such :
De Braekeleer E, Huret JL, Mossafa H, Hautaviita K, Dessen P
Internet databases and resources for cytogenetics and cytogenomic
Atlas Genet Cytogenet Oncol Haematol. in press
On line version : http://AtlasGeneticsOncology.org/Deep/Internet_DatabasesID20143.htm

© Atlas of Genetics and Cytogenetics in Oncology and Haematology
indexed on : Wed Aug 2 16:13:32 CEST 2017


Home   Genes    Leukemias    Solid Tumors    Cancer-Prone    Deep Insight    Case Reports    Journals   Portal    Teaching   

X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 NA

For comments and suggestions or contributions, please contact us

jlhuret@AtlasGeneticsOncology.org.