Giuseppe Narzisi, Ph.D.
Affiliations: | 2011 | Computer Science | New York University, New York, NY, United States |
Area:
Bioinformatics Biology, Computer ScienceGoogle:
"Giuseppe Narzisi"Parents
Sign in to add mentor| Bud Mishra | grad student | 2011 | NYU | |
| (Scoring-and-Unfolding Trimmed Tree Assembler: Algorithms for Assembling Genome Sequences Accurately and Efficiently.) | ||||
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Publications
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| McDaniel JH, Patel V, Olson ND, et al. (2024) Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair for somatic benchmarks. Biorxiv : the Preprint Server For Biology |
| Park J, Cook DE, Chang PC, et al. (2024) DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies. Biorxiv : the Preprint Server For Biology |
| Keskus A, Bryant A, Ahmad T, et al. (2024) Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads. Medrxiv : the Preprint Server For Health Sciences |
| Wagner J, Olson ND, Harris L, et al. (2022) Benchmarking challenging small variants with linked and long reads. Cell Genomics. 2 |
| Byrska-Bishop M, Evani US, Zhao X, et al. (2022) High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Cell. 185: 3426-3440.e19 |
| Wagner J, Olson ND, Harris L, et al. (2022) Curated variation benchmarks for challenging medically relevant autosomal genes. Nature Biotechnology |
| Foox J, Tighe SW, Nicolet CM, et al. (2021) Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. Nature Biotechnology. 39: 1129-1140 |
| Musunuri R, Arora K, Corvelo A, et al. (2020) Somatic variant analysis of linked-reads sequencing data with Lancet. Bioinformatics (Oxford, England) |
| Chapman LM, Spies N, Pai P, et al. (2020) A crowdsourced set of curated structural variants for the human genome. Plos Computational Biology. 16: e1007933 |
| Dolzhenko E, Bennett MF, Richmond PA, et al. (2020) ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data. Genome Biology. 21: 102 |