Comparison of double-stranded and single-stranded cfDNA library construction methods for targeted genome and methylation sequencing

Cell-free DNA (cfDNA) profiling by deep sequencing (i.e., by next generation sequencing (NGS)) has wide applications in cancer diagnosis, prognosis, and therapy response monitoring. One key step of cfDNA deep sequencing workflow is NGS library construction, whose efficiency significantly affects the utilization efficiency of cfDNA molecules, and eventually determines effective sequencing depth and sequencing accuracy. In this study, we compared two different types of cfDNA library construction methods, namely double-stranded library (dsLib, the conventional method which captures dsDNA molecules) and single-stranded library (ssLib) preparation, which captures ssDNA molecules, for the applications of mutation detection and methylation profiling, respectively. Our results suggest that the dsLib method was suitable for mutation detection while the ssLib method proved more efficient for methylation analysis. Our findings could help researchers choose the more appropriate library construction method for corresponding downstream applications of cfDNA sequencing.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions

Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations.

Chimeric crRNA improves CRISPR–Cas12a specificity in the N501Y mutation detection of Alpha, Beta, Gamma, and Mu variants of SARS-CoV-2

Many CRISPR/Cas platforms have been established for the detection of SARS-CoV-2. But the detection platform of the variants of SARS-CoV-2 is scarce because its specificity is very challenging to achieve for those with only one or a few nucleotide(s) differences. Here, we report for the first time that chimeric crRNA could be critical in enhancing the specificity of CRISPR-Cas12a detecting of N501Y, which is shared by Alpha, Beta, Gamma, and Mu variants of SARS-CoV-2 without compromising its sensitivity. This strategy could also be applied to detect other SARS-CoV-2 variants that differ only one or a few nucleotide(s) differences.

Interpreting dN/dS under different selective regimes in cancer evolution

The standard relationship between the dN/dS statistic and the selection coefficient is contingent upon the computation of the rate of fixation of non-synonymous and synonymous mutations among divergent lineages (substitutions). In cancer genomics, however, dN/dS is typically calculated by including mutations that are still segregating in the cell population. The interpretation of dN/dS within sexual populations has been shown to be problematic. Here we used a simple model of somatic evolution to study the relationship between dN/dS and the selection coefficient in the presence of deleterious, neutral, and beneficial mutations in cancer. We found that dN/dS can be used to distinguish cancer genes under positive or negative selection, but it is not always informative about the magnitude of the selection coefficient. In particular, under the asexual scenario simulated, dN/dS is insensitive to negative selection strength. Furthermore, the relationship between dN/dS and the positive selection coefficient depends on the mutation detection threshold, and, in particular scenarios, it can become non-linear. Our results warn about the necessary caution when interpreting the results drawn from dN/dS estimates in cancer.