Abasov, R. | Search bioRxiv

bioRxiv2021

APOBEC mutagenesis is low in most types of non-B DNA structures, unlike other types of cancer mutagenesis

While somatic mutations are known to be enriched in genome regions with non-canonical DNA secondary structure, the impact of particular mutagens still needs to be elucidated. Here, we demonstrate that in human cancers, the APOBEC mutagenesis is not enriched in direct repeats, mirror repeats, short tandem repeats, and G-quadruplexes, and even decreased below its level in B-DNA for cancer samples with very high APOBEC activity. In contrast, we observe that the APOBEC-induced mutational density is positively associated with APOBEC activity in inverted repeats (cruciform structures), where the impact of cytosine at the 3-end of the hairpin loop is substantial. Surprisingly, the APOBEC-signature mutation density per TC motif in the single-stranded DNA of a G-quadruplex (G4) is lower than in the four-stranded part of G4 and in B-DNA. The APOBEC mutagenesis, as well as the UV-mutagenesis in melanoma samples are absent in Z-DNA regions, due to depletion of their mutational signature motifs.

cancer biology↗

bioRxiv2021

Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery

Accurate variant detection in the coding regions of the human genome is a key requirement for molecular diagnostics of Mendelian disorders. Efficiency of variant discovery from nextgeneration sequencing (NGS) data depends on multiple factors, including reproducible coverage biases of NGS methods and the performance of read alignment and variant calling software. In this work, we systematically evaluated the performance of 4 popular short read aligners (Bowtie2, BWA, Isaac, and Novoalign) and 6 variant calling and filtering methods (based on DeepVariant, Genome Analysis ToolKit (GATK), FreeBayes, and Strelka2) using a set of 10 "gold standard" WES and WGS datasets. Our results suggest that Bowtie2 performs significantly worse than other aligners and should not be used for medical variant calling. When other aligners were used, the accuracy of variant discovery mostly depended on the variant caller and not the read aligner. Among the tested variant callers, DeepVariant consistently showed the best performance and the highest robustness, with other state-of-the-art tools, i.e. Strelka2 and GATK with 1D convolutional neural network variant scoring, also showing high performance on both WES and WGS data. The results show surprisingly large differences in the performance of cutting-edge tools even on high confidence regions of the coding genome. This highlights the importance of regular benchmarking of quickly evolving tools and pipelines. Finally, we discuss the need for a more diverse set of gold standard genomes (e.g. of African, Hispanic, or mixed ancestry) that would allow to control for deep learning model overfitting. For similar reasons there is a need for better variant caller assessment in the repetitive regions of the coding genome.

bioinformatics↗