Precise detection of de novo single nucleotide variants in human genomes

Significance The precise location of variants in the human genome is of utmost importance. We present a unique approach, coverage-based single nucleotide variant (SNV) identification (COBASI), which uses only perfect matches between the reads of a sequence project and a reference genome to detect and accurately identify de novo SNVs. From the perfect matches, a representation of the read coverage per nucleotide along the genome, the variation landscape, is generated. SNVs are then pinpointed as significant changes in coverage and de novo SNVs can be identified with high precision. The performance of COBASI was analyzed using simulations and experimentally validated by sequencing de novo SNVs identified from a parent–offspring trio. We propose this pipeline as a useful tool for different genomic applications. The precise determination of de novo genetic variants has enormous implications across different fields of biology and medicine, particularly personalized medicine. Currently, de novo variations are identified by mapping sample reads from a parent–offspring trio to a reference genome, allowing for a certain degree of differences. While widely used, this approach often introduces false-positive (FP) results due to misaligned reads and mischaracterized sequencing errors. In a previous study, we developed an alternative approach to accurately identify single nucleotide variants (SNVs) using only perfect matches. However, this approach could be applied only to haploid regions of the genome and was computationally intensive. In this study, we present a unique approach, coverage-based single nucleotide variant identification (COBASI), which allows the exploration of the entire genome using second-generation short sequence reads without extensive computing requirements. COBASI identifies SNVs using changes in coverage of exactly matching unique substrings, and is particularly suited for pinpointing de novo SNVs. Unlike other approaches that require population frequencies across hundreds of samples to filter out any methodological biases, COBASI can be applied to detect de novo SNVs within isolated families. We demonstrate this capability through extensive simulation studies and by studying a parent–offspring trio we sequenced using short reads. Experimental validation of all 58 candidate de novo SNVs and a selection of non-de novo SNVs found in the trio confirmed zero FP calls. COBASI is available as open source at https://github.com/Laura-Gomez/COBASI for any researcher to use.