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Abstract The Context-dependent Mutation Analysis Package and Visualization Software (CDMAP/CDVIS) is an automated, modular toolkit used for the analysis and visualization of context-dependent mutation patterns (site-specific variation in mutation rate from neighboring-nucleotide effects). The CDMAP computes context-dependent mutation rates using a Variant Call File (VCF), Genbank file, and reference genome and can generate high-resolution figures to analyze variation in mutation rate across spatiotemporal scales. This algorithm has been benchmarked against mutation accumulation data but can also be used to calculate context-dependent mutation rates for polymorphism or closely related species as long as the input requirements are met. Output from CDMAP can be integrated into CDVIS, an interactive database for visualizing mutation patterns across multiple taxa simultaneously. 

With fast-growing polymerase chain reaction (PCR) technologies and various application methods, the technique has benefited science and medical fields. While having strengths and limitations on each technology, there are not many studies comparing the efficiency and specificity of PCR technologies. The objective of this review is to summarize a large amount of scattered information on PCR technologies focused on the two majorly used technologies: qPCR (quantitative polymerase chain reaction) and ddPCR (droplet-digital polymerase chain reaction). Here we analyze and compare the two methods for (1) efficiency, (2) range of detection and limitations under different disciplines and gene targets, (3) optimization, and (4) status on antibiotic resistance genes (ARGs) analysis. It has been identified that the range of detection and quantification limit varies depending on the PCR method and the type of sample. Careful optimization of target gene analysis is essential for building robust analysis for both qPCR and ddPCR. In our era where mutation of genes may lead to a pandemic of viral infectious disease or antibiotic resistance-induced health threats, this study hopes to set guidelines for meticulous detection, quantification, and analysis to help future prevention and protection of global health, the economy, and ecosystems. 

Abstract Understanding how mutations affect survivability is a key component to knowing how organisms and complex traits evolve. However, most mutations have a minor effect on fitness and these effects are difficult to resolve using traditional molecular techniques. Therefore, there is a dire need for more accurate and precise fitness measurements methods. Here, we measured the fitness effects in Burkholderia cenocepacia HI2424 mutation accumulation (MA) lines using droplet-digital polymerase chain reaction (ddPCR). Overall, the fitness measurements from ddPCR-MA are correlated positively with fitness measurements derived from traditional phenotypic marker assays (r = 0.297, P = 0.05), but showed some differences. First, ddPCR had significantly lower measurement variance in fitness (F = 3.78, P < 2.6 × 10−13) in control experiments. Second, the mean fitness from ddPCR-MA measurements were significantly lower than phenotypic marker assays (−0.0041 vs −0.0071, P = 0.006). Consistent with phenotypic marker assays, ddPCR-MA measurements observed multiple (27/43) lineages that significantly deviated from mean fitness, suggesting that a majority of the mutations are neutral or slightly deleterious and intermixed with a few mutations that have extremely large effects. Of these mutations, we found a significant excess of mutations within DNA excinuclease and Lys R transcriptional regulators that have extreme deleterious and beneficial effects, indicating that modifications to transcription and replication may have a strong effect on organismal fitness. This study demonstrates the power of ddPCR as a ubiquitous method for high-throughput fitness measurements in both DNA- and RNA-based organisms regardless of cell type or physiology.