The ARKdb: genome databases for farmed and other animals

Hu, Jian; Mungall, Chris; Law, Andy; Papworth, R; Nelson, Jacob P.; Brown, Anna; Simpson, Irene; Leckie, Shirley A.; Burt, David W.; Hillyard, A.L.; Archibald, Alan

doi:10.1093/nar/29.1.106

Cited by 61 publications

(32 citation statements)

References 3 publications

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“…The base map for displaying marker locations was developed with mapping data mainly from USDA-MARC (Meat and Animal Research Center) (Rohrer et al 1996). Markers defining a QTL that were not on the MARC map were interpolated to form a consensus map with the comparative framework of ArkDB (Hu et al 2001). When a marker defining the peak, the left, or the right boundary of a QTL is found to be on the MARC map, the MARC map location of this marker is inputted directly into the PigQTLdb.…”

Section: Methodsmentioning

confidence: 99%

A QTL resource and comparison tool for pigs: PigQTLDB

et al. 2005

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During the past decade, efforts to map quantitative trait loci (QTL) in pigs have resulted in hundreds of QTL being reported for growth, meat quality, reproduction, disease resistance, and other traits. It is a challenge to locate, interpret, and compare QTL results from different studies. We have developed a pig QTL database (PigQTLdb) that integrates available pig QTL data in the public domain, thus, facilitating the use of this QTL data in future studies. We also developed a pig trait classification system to standardize names of traits and to simplify organization and searching of the trait data. These steps made it possible to compare primary data from diverse sources and methods. We used existing pig map databases and other publicly available data resources (such as PubMed) to avoid redundant developmental work. The PigQTLdb was also designed to include data representing major genes and markers associated with a large effect on economically important traits. To date, over 790 QTL from 73 publications have been curated into the database. Those QTL cover more than 300 different traits. The data have been submitted to the Entrez Gene and the Map Viewer resources at NCBI, where the information about markers was matched to marker records in NCBI's UniSTS database. Having these data in a public resource like NCBI allows regularly updated automatic matching of markers to public sequence data by e-PCR. The submitted data, and the results of these calculations, are retrievable from NCBI via Entrez Gene, Map Viewer, and UniSTS. Efforts were undertaken to improve the integrated functional genomics resources for pigs. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Authors Zhi-Liang AbstractDuring the past decade, efforts to map quantitative trait loci (QTL) in pigs have resulted in hundreds of QTL being reported for growth, meat quality, reproduction, disease resistance, and other traits. It is a challenge to locate, interpret, and compare QTL results from different studies. We have developed a pig QTL database (PigQTLdb) that integrates available pig QTL data in the public domain, thus, facilitating the use of this QTL data in future studies. We also developed a pig trait classification system to standardize names of traits and to simplify organization and searching of the trait data. These steps made it possible to compare primary data from diverse sources and methods. We used existing pig map databases and other publicly available data resources (such as PubMed) to avoid redundant developmental work. The PigQTLdb was also designed to include data representing major genes and markers associated with a large effect on economically important traits. To date, over 790 QTL from 73 publications have been curated into the database. Those QTL cover more than 300 different traits. The data have been submitted to the Entrez Gene and the Map Viewer resources at NCBI, where the information a...

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Section: Methodsmentioning

confidence: 99%

A QTL resource and comparison tool for pigs: PigQTLDB

et al. 2005

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“…For instance, most of the single-locus traits in livestock are listed in the OMIA (Online Mendelian Inheritance in Animals) database. Other databases have been constructed in UK (Hu et al, 2001), Australia (Hawken et al, 2004) and France (Table 1).…”

Section: Potential Benefits Of Genomicsmentioning

confidence: 99%

Recent advances in cattle functional genomics and their application to beef quality

Hocquette

Lehnert

Barendse

et al. 2007

Animal

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The advent of high-throughput DNA sequencing techniques, array technology and protein analysis has increased the efficiency of research in bovine muscle physiology, with the ultimate objective of improving beef quality either by breeding or rearing factors. For genetic purposes, polymorphisms in some key genes have been reported for their association with beef quality traits. The sequencing of the bovine genome has dramatically increased the number of available gene polymorphisms. The association of these new polymorphisms with the variability in beef quality (e.g. tenderness, marbling) for different breeds in different rearing systems will be a very important issue. For rearing purposes, global gene expression profiling at the mRNA or protein level has already shown that previously unsuspected genes may be associated either with muscle development or growth, and may lead to the development of new molecular indicators of tenderness or marbling. Some of these genes are specifically regulated by genetic and nutritional factors or differ between different beef cuts. In recognition of the potential economic benefits of genomics, public institutions in association with the beef industry are developing livestock genomics projects around the world. From the scientific, technical and economical points of view, genomics is thus reshaping research on beef quality.

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“…This objective can be reached by the use of genetic maps that are sufficiently saturated by molecular markers. The mapping approach seems to be realistic due to availability of advanced pig maps at the present time (Hu et al, 2001;Rink et al, 2002;Rohrer et al, 2002). Particular scanning of the MeLiM genome by microsatellite analysis is in progress (in CEA-INRA, Laboratoire de Radiobiologie et d'Etude du Genome, Jouy-en-Josas, France) with aim to reveal melanoma predisposition genes.…”

Section: Resultsmentioning

confidence: 99%

Inheritance of malignant melanoma in the melim strain of miniature pigs

Hruban¹,

Horák²,

Fortýn³

et al. 2004

Vet. Med. - Czech

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Selective breeding of miniature pigs bearing cutaneous tumours resulted in the establishment of the MeLiM strain with hereditary malignant melanoma. The inheritance of melanoma was tested in a two-generation kindred comprising 456 progeny from 78 li�ers. Melanomas were recognisable visually as black-pigmented lesions on the skin. Their size, shape, number, progression and metastatic spreading varied widely. All possible melanoma forms known in human melanoma, i.e. pigmented naevi, dysplastic naevi, superficial spreading melanoma and nodular malignant melanoma, were found in the MeLiM pigs. Because the occurrence of nodular malignant melanoma segregated in all recorded li�ers we have included only this form in the genetic analysis. The tumours were nodular with exophytic growth over the skin surface. All showed similar histopathological features, vertical growth to muscle fascia and high metastatic activity. We hypothesize, on the basis of segregation ratios obtained from various mating types, that the mode of inheritance of nodular melanoma in the MeLiM strain is probably controlled by three genes.

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The ARKdb: genome databases for farmed and other animals

Cited by 61 publications

References 3 publications

A QTL resource and comparison tool for pigs: PigQTLDB

A QTL resource and comparison tool for pigs: PigQTLDB

Recent advances in cattle functional genomics and their application to beef quality

Inheritance of malignant melanoma in the melim strain of miniature pigs

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