The co-existence of wild-type and mutated mitochondrial DNA (mtDNA) molecules termed heteroplasmy becomes a research hot point of mitochondria. In this review, we listed several methods of mtDNA heteroplasmy research, including the enrichment of mtDNA and the way of calling heteroplasmic variations. At the present, while calling the novel ultra-low level heteroplasmy, high-throughput sequencing method is dominant while the detection limit of recorded mutations is accurate to 0.01% using the other quantitative approaches. In the future, the studies of mtDNA heteroplasmy may pay more attention to the single-cell level and focus on the linkage of mutations.
Multiple displacement amplification (MDA), a most popular isothermal whole genome amplification (WGA) method, suffers the major hurdle of highly uneven amplification, thus, leading to many problems in approaching biological applications related to copy-number assessment. In addition to the optimization of reagents and conditions, complete physical separation of the entire reaction system into numerous tiny chambers or droplets using microfluidic devices, has been proven efficient to mitigate this amplifying bias in recent works. Here, we present another MDA advance, microchannel MDA (μcMDA), which decentralizes MDA reagents throughout a one-dimensional slender tube. Due to the double effect from soft partition of high molecular-weight DNA molecules and less-limited diffusion of small particles, μcMDA is shown to be significantly effective at improving the amplification uniformity, which enables us to accurately detect single nucleotide variants (SNVs) with higher efficiency and sensitivity. More importantly, this straightforward method requires neither customized instruments nor complicated operations, making it a ready-to-use technique in almost all biological laboratories.
Multiple displacement amplification (MDA) is considered to be a conventional approach to comprehensive amplification from low input DNA. The chimeric reads generated in MDA lead to severe disruption in some studies, including those focusing on heterogeneity, structural variation, and genetic recombination. Meanwhile, the generation of by-products gives a new approach to gain insights into the reaction process of φ29 polymerase. Here, we analyzed 36.7 million chimeras and screened 196 billion chimeric hotspots in the human genome, as well as evaluating the hotspot selective preference of chimeras. No significant preference was captured in the distributions of chimeras and hotspots among chromosomes. Hotspots with overlaps for 12–13 nucleotides (nt) were most likely to be selected as templates in chimera generation. Meanwhile, a regularly selective preference was noticed in overlap GC content. The preferences in overlap length and GC content was shown to be pertinent to the sequence denaturation temperature, which pointed out the optimization direction for reducing chimeras. Distance preference between two segments of chimeras was 80–280 nt. The analysis is beneficial for reducing the chimeras in MDA, and the characterization of MDA chimeras is helpful in distinguishing MDA chimeras from chimeric sequences caused by disease.
We present a user-friendly and transferable genome-wide DNA G-quadruplex (G4) profiling method that identifies G4 structures from ordinary whole-genome resequencing data by seizing the slight fluctuation of sequencing quality. In the human genome, 736,689 G4 structures were identified, of which 45.9% of all predicted canonical G4-forming sequences were characterized. Over 89% of the detected canonical G4s were also identified by combining polymerase stop assays with next-generation sequencing. Testing using public datasets of 6 species demonstrated that the present method is widely applicable. The detection rates of predicted canonical quadruplexes ranged from 32% to 58%. Because single nucleotide variations (SNVs) influence the formation of G4 structures and have individual differences, the given method is available to identify and characterize G4s genome-wide for specific individuals.
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.