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Graph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference to represent the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based toolkits for NGS read alignment and variant calling, methods to curate genomic variants and subsequently construct genome graphs remain an understudied problem that inevitably determines the effectiveness of the overall bioinformatics pipeline. In this study, we discuss obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and demonstrate this approach on the whole-genome samples of African ancestry. Our results show that population-specific graphs, as more representative alternatives to linear or generic graph references, can achieve significantly lower read mapping errors and enhanced variant calling sensitivity, in addition to providing the improvements of joint variant calling without the need of computationally intensive post-processing steps., (© 2022. The Author(s).)

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.

Ultra-compliant neural probes implanted into tissue using a molded, biodissolvable sodium carboxymethyl cellulose (Na-CMC)-saccharide composite needle delivery vehicle are subjected to fluid-structure interactions that can displace the recording site of the probe with respect to its designed implant location. We applied particle velocimetry to analyze the behavior of ultra-compliant structures under different implantation conditions for a range of CMC-based materials and identified a fluid management protocol that resulted in the successful targeted depth placement of the recording sites.