Browsing by Author "Ricketts, C."

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    Author Correction: A robust benchmark for detection of germline large deletions and insertions
    (Nature Research, 2020) Zook, J. M.; Hansen, N. F.; Olson, N. D.; Chapman, L.; Mullikin, J. C.; Xiao, C.; Sherry, S.; Koren, S.; Phillippy, A. M.; Boutros, P. C.; Sahraeian, S. M. E.; Huang, V.; Rouette, A.; Alexander, N.; Mason, C. E.; Hajirasouliha, I.; Ricketts, C.; Lee, J.; Tearle, R.; Fiddes, I. T.; Barrio, A. M.; Wala, J.; Carroll, A.; Ghaffari, N.; Rodriguez, O. L.; Bashir, A.; Jackman, S.; Farrell, J. J.; Wenger, A. M.; Alkan, Can; Söylev, A.; Schatz, M. C.; Garg, S.; Church, G.; Marschall, T.; Chen, K.; Fan, X.; English, A. C.; Rosenfeld, J. A.; Zhou, W.; Mills, R. E.; Sage, J. M.; Davis, J. R.; Kaiser, M. D.; Oliver, J. S.; Catalano, A. P.; Chaisson, M. J. P.; Spies, N.; Sedlazeck, F. J.; Salit, M.
    New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping.
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    VALOR2: characterization of large-scale structural variants using linked-reads
    (BioMed Central Ltd., 2020-03) Karaoğlanoğlu, Fatih; Ricketts, C.; Ebren, Ezgi; Rasekh, M. E.; Hajirasouliha, I.; Alkan, Can
    Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2.