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The repository contains reports (Projects, Bachelor or Master Theses, Technical Notes, ...) prepared by students and staff working with Prof. Scholz at Hamburg University of Applied Sciences (HAW Hamburg). AERO is part of the Department of Automotive and Aeronautical Engineering in the Faculty of Engineering and Computer Science and of the Strategic Research Area Mobility & Transport. The interface is available in English.
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mirDNMR is a database for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). In conclusion, mirDNMR can be widely used to identify the genetic basis of sporadic genetic diseases.
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- Data Repository Germany Compatibility:Not yet registeredPartners:IKB, KIT, DFG, IMT, University of Paderborn +1 partners Humanities and Social Sciences, Fine Arts, Music, Theatre and Media Studies, HumanitiesIKB,KIT,DFG,IMT,University of Paderborn,IKB
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The repository contains reports (Projects, Bachelor or Master Theses, Technical Notes, ...) prepared by students and staff working with Prof. Scholz at Hamburg University of Applied Sciences (HAW Hamburg). AERO is part of the Department of Automotive and Aeronautical Engineering in the Faculty of Engineering and Computer Science and of the Strategic Research Area Mobility & Transport. The interface is available in English.
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mirDNMR is a database for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). In conclusion, mirDNMR can be widely used to identify the genetic basis of sporadic genetic diseases.
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