The proposed PDA-MS/UniQ method pursues a much smaller number of primers set compared with conventional PCR. In the simulation experiment for amplifying 12 669 target sequences, the performance of our method with 68% reduction on required mu-primers number seems to be superior to the compared heuristic approaches in both computation efficiency and reduction percentage. Our integrated PDA-MS/UniQ method is applied to the differential detection on 9 plant viruses from 4 genera with MMA and PAH of 11 mu-primers instead of 18 unique ones in conventional PCR while amplifying overall 9 target sequences. The results of wet lab experiments with integrated MMA-PAH system have successfully validated the specificity and sensitivity of the primers/probes designed with our integrated PDA-MS/UniQ method.
BackgroundMitochondrial dysfunction is associated with various aging diseases. The copy number of mtDNA in human cells may therefore be a potential biomarker for diagnostics of aging. Here we propose a new computational method for the accurate assessment of mtDNA copies from whole genome sequencing data.ResultsTwo families of the human whole genome sequencing datasets from the HapMap and the 1000 Genomes projects were used for the accurate counting of mitochondrial DNA copy numbers. The results revealed the parental mitochondrial DNA copy numbers are significantly lower than that of their children in these samples. There are 8%~21% more copies of mtDNA in samples from the children than from their parents. The experiment demonstrated the possible correlations between the quantity of mitochondrial DNA and aging-related diseases.ConclusionsSince the next-generation sequencing technology strives to deliver affordable and non-biased sequencing results, accurate assessment of mtDNA copy numbers can be achieved effectively from the output of whole genome sequencing. We implemented the method as a software package MitoCounter with the source code and user's guide available to the public at http://sourceforge.net/projects/mitocounter/.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.