ArticleScientific reports2015
SiNG-PCRseq: Accurate inter-sequence quantification achieved by spiking-in a neighbor genome for competitive PCR amplicon sequencing.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
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- Article
- Simultaneous Methylation-Level Assessment of Hundreds of CpG Sites by Targeted Bisulfite PCR Sequencing (TBPseq).Frontiers in genetics · 2017Article
- Concurrent progress of reprogramming and gene correction to overcome therapeutic limitation of mutant ALK2-iPSC.Experimental & molecular medicine · 2016Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the recent technological advances in DNA quantitation by sequencing, accurate delineation of the quantitative relationship among different DNA sequences is yet to be elaborated due to difficulties in correcting the sequence-specific quantitation biases. We here developed a novel DNA quantitation method via spiking-in a neighbor genome for competitive PCR amplicon sequencing (SiNG-PCRseq). This method utilizes genome-wide chemically equivalent but easily discriminable homologous sequences with a known copy arrangement in the neighbor genome. By comparing the amounts of selected human DNA sequences simultaneously to those of matched sequences in the orangutan genome, we could accurately draw the quantitative relationships for those sequences in the human genome (root-mean-square deviations <0.05). Technical replications of cDNA quantitation performed using different reagents at different time points also resulted in excellent correlations (R(2) > 0.95). The cDNA quantitation using SiNG-PCRseq was highly concordant with the RNA-seq-derived version in inter-sample comparisons (R(2) = 0.88), but relatively discordant in inter-sequence quantitation (R(2) < 0.44), indicating considerable level of sequence-dependent quantitative biases in RNA-seq. Considering the measurement structure explicitly relating the amount of different sequences within a sample, SiNG-PCRseq will facilitate sharing and comparing the quantitation data generated under different spatio-temporal settings.
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