Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.13.380741v1?rss=1 Authors: Olson, N. D., Wagner, J., McDaniel, J., Stephens, S. H., Westreich, S. T., Prasanna, A. G., Johanson, E., Boja, E., Maier, E. J., Serang, O., Jaspez, D., Lorenzo-Salazar, J. M., Munoz-Barrera, A., Rubio-Rodriguez, L. A., Flores, C., Kyriakidis, K., Malousi, A., Shafin, K., Pesout, T., Jain, M., Paten, B., Chang, P.-C., Kolesnikov, A., Nattestad, M., Baid, G., Goel, S., Yang, H., Carroll, A., Eveleigh, R., Bourgey, M., Bourque, G., Li, G., MA, C., Tang, L., DU, Y., Zhang, S., Morata, J., Tonda, R., Parra, G., Trotta, J.-R., Brueffer, C., Demirkaya-Budak, S., Kabakci-Zorlu, D., Turgut, D., Kalay Abstract: The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new 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. Copy rights belong to original authors. Visit the link for more info