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Konferenzbeitrag

MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons56693

Gokhale,  Chaitanya S.
Department Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, Max Planck Society;
Research Group Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Volltexte (frei zugänglich)

Bhardwaj_BMCBioinformatics_2009.pdf
(Verlagsversion), 397KB

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Zitation

Bhardwaj, A., Mukerji, M., Sharma, S., Paul, J., Gokhale, C. S., Srivastava, A. K., et al. (2009). MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease. BMC Bioinformatics, 10(Suppl. 8): S7.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-D577-E
Zusammenfassung
Human mitochondrial DNA (mtDNA) variations have been implicated in a broad spectrum of diseases. With over 3000 mtDNA variations reported across databases, establishing pathogenicity of variations in mtDNA is a major challenge. We have designed and developed a comprehensive weighted scoring system (MtSNPscore) for identification of mtDNA variations that can impact pathogenicity and would likely be associated with disease. The criteria for pathogenicity include information available in the literature, predictions made by various in silico tools and frequency of variation in normal and patient datasets. The scoring scheme also assigns scores to patients and normal individuals to estimate the cumulative impact of variations. The method has been implemented in an automated pipeline and has been tested on Indian ataxia dataset (92 individuals), sequenced in this study, and other publicly available mtSNP dataset comprising of 576 mitochondrial genomes of Japanese individuals from six different groups, namely, patients with Parkinson's disease, patients with Alzheimer's disease, young obese males, young non-obese males, and type-2 diabetes patients with or without severe vascular involvement. MtSNPscore, for analysis can extract information from variation data or from mitochondrial DNA sequences. It has a web-interface http://bioinformatics.ccmb.res.in/cgi-bin/snpscore/Mtsnpscore.pl webcite that provides flexibility to update/modify the parameters for estimating pathogenicity.