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Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms-GATK HaplotypeCaller, DeepTrio and Velsera GRAF-exploring the assumption that a requirement of consensus can serve as an effective filter for high-quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large-scale analyses without need for manual intervention., Competing Interests: COMPETING INTEREST STATEMENT Özem Kalay, Sinem Demirkaya-Budak, Deniz Turgut, Gungor Budak, Elif Arslan, Vladimir Semenyuk, Brandi Davis-Dusenbery and Amit Jain were employees of Velsera Inc. throughout the study period.

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants., Competing Interests: DECLARATION OF INTERESTS C.B. is an employee and shareholder of SAGA Diagnostics AB. A.C., P.-C.C., A.K., M.N., G.B., S.G., and H.Y. are employees of Google, and A.C. is a shareholder. S.D.-B., D.K.-Z., D.T., Ö.K., G.B., K.N., E.A., R.B., I.J.J., A.D., V.S., A.J., and H.S.T. are employees of Seven Bridges Genomics. O.S. and S. T.W. are employees of DNAnexus. G.L., C.M., L.T.F., Y.D., and S.Z. are employees of Genetalks. V.J., M.R., B.L., C.R., S.C., and R.M. are employees of Illumina. S.M.E.S. and M.M. are employees of Roche. C.H. is an employee of Wasai Technology. H.F., Z.L., and L.C. are employees of Sentieon.