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  1. Article: Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms.

    Behera, Sairam / Catreux, Severine / Rossi, Massimiliano / Truong, Sean / Huang, Zhuoyi / Ruehle, Michael / Visvanath, Arun / Parnaby, Gavin / Roddey, Cooper / Onuchic, Vitor / Cameron, Daniel L / English, Adam / Mehtalia, Shyamal / Han, James / Mehio, Rami / Sedlazeck, Fritz J

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of ... ...

    Abstract Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with ~30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications.
    Language English
    Publishing date 2024-01-06
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.02.573821
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions.

    Olson, Nathan D / Wagner, Justin / McDaniel, Jennifer / Stephens, Sarah H / Westreich, Samuel T / Prasanna, Anish G / Johanson, Elaine / Boja, Emily / Maier, Ezekiel J / Serang, Omar / Jáspez, David / Lorenzo-Salazar, José M / Muñoz-Barrera, Adrián / Rubio-Rodríguez, Luis A / Flores, Carlos / Kyriakidis, Konstantinos / Malousi, Andigoni / Shafin, Kishwar / Pesout, Trevor /
    Jain, Miten / Paten, Benedict / Chang, Pi-Chuan / Kolesnikov, Alexey / Nattestad, Maria / Baid, Gunjan / Goel, Sidharth / Yang, Howard / Carroll, Andrew / Eveleigh, Robert / Bourgey, Mathieu / Bourque, Guillaume / Li, Gen / Ma, ChouXian / Tang, LinQi / Du, YuanPing / Zhang, ShaoWei / Morata, Jordi / Tonda, Raúl / Parra, Genís / Trotta, Jean-Rémi / Brueffer, Christian / Demirkaya-Budak, Sinem / Kabakci-Zorlu, Duygu / Turgut, Deniz / Kalay, Özem / Budak, Gungor / Narcı, Kübra / Arslan, Elif / Brown, Richard / Johnson, Ivan J / Dolgoborodov, Alexey / Semenyuk, Vladimir / Jain, Amit / Tetikol, H Serhat / Jain, Varun / Ruehle, Mike / Lajoie, Bryan / Roddey, Cooper / Catreux, Severine / Mehio, Rami / Ahsan, Mian Umair / Liu, Qian / Wang, Kai / Sahraeian, Sayed Mohammad Ebrahim / Fang, Li Tai / Mohiyuddin, Marghoob / Hung, Calvin / Jain, Chirag / Feng, Hanying / Li, Zhipan / Chen, Luoqi / Sedlazeck, Fritz J / Zook, Justin M

    Cell genomics

    2022  Volume 2, Issue 5

    Abstract: 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 ...

    Abstract 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.
    Language English
    Publishing date 2022-04-27
    Publishing country United States
    Document type Journal Article
    ISSN 2666-979X
    ISSN (online) 2666-979X
    DOI 10.1016/j.xgen.2022.100129
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: PROMO: Real-time prospective motion correction in MRI using image-based tracking.

    White, Nathan / Roddey, Cooper / Shankaranarayanan, Ajit / Han, Eric / Rettmann, Dan / Santos, Juan / Kuperman, Josh / Dale, Anders

    Magnetic resonance in medicine

    2009  Volume 63, Issue 1, Page(s) 91–105

    Abstract: Artifacts caused by patient motion during scanning remain a serious problem in most MRI applications. The prospective motion correction technique attempts to address this problem at its source by keeping the measurement coordinate system fixed with ... ...

    Abstract Artifacts caused by patient motion during scanning remain a serious problem in most MRI applications. The prospective motion correction technique attempts to address this problem at its source by keeping the measurement coordinate system fixed with respect to the patient throughout the entire scan process. In this study, a new image-based approach for prospective motion correction is described, which utilizes three orthogonal two-dimensional spiral navigator acquisitions, along with a flexible image-based tracking method based on the extended Kalman filter algorithm for online motion measurement. The spiral navigator/extended Kalman filter framework offers the advantages of image-domain tracking within patient-specific regions-of-interest and reduced sensitivity to off-resonance-induced corruption of rigid-body motion estimates. The performance of the method was tested using offline computer simulations and online in vivo head motion experiments. In vivo validation results covering a broad range of staged head motions indicate a steady-state error of less than 10% of the motion magnitude, even for large compound motions that included rotations over 15 deg. A preliminary in vivo application in three-dimensional inversion recovery spoiled gradient echo (IR-SPGR) and three-dimensional fast spin echo (FSE) sequences demonstrates the effectiveness of the spiral navigator/extended Kalman filter framework for correcting three-dimensional rigid-body head motion artifacts prospectively in high-resolution three-dimensional MRI scans.
    MeSH term(s) Algorithms ; Artifacts ; Brain/anatomy & histology ; Computer Systems ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging/methods ; Motion ; Movement ; Reproducibility of Results ; Sensitivity and Specificity ; Software
    Language English
    Publishing date 2009-12-21
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 605774-3
    ISSN 1522-2594 ; 0740-3194
    ISSN (online) 1522-2594
    ISSN 0740-3194
    DOI 10.1002/mrm.22176
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

    Olson, Nathan D. / Wagner, Justin / McDaniel, Jennifer / Stephens, Sarah H. / Westreich, Samuel T. / Prasanna, Anish G. / Johanson, Elaine / Boja, Emily / Maier, Ezekiel J. / Serang, Omar / Jáspez, David / Lorenzo-Salazar, José M. / Muñoz-Barrera, Adrián / Rubio-Rodríguez, Luis A. / Flores, Carlos / Kyriakidis, Konstantinos / Malousi, Andigoni / Shafin, Kishwar / Pesout, Trevor /
    Jain, Miten / Paten, Benedict / Chang, Pi-Chuan / Kolesnikov, Alexey / Nattestad, Maria / Baid, Gunjan / Goel, Sidharth / Yang, Howard / Carroll, Andrew / Eveleigh, Robert / Bourgey, Mathieu / Bourque, Guillaume / Li, Gen / ChouXian, MA / Tang, LinQi / YuanPing, DU / Zhang, ShaoWei / Morata, Jordi / Tonda, Raúl / Parra, Genís / Trotta, Jean-Rémi / Brueffer, Christian / Demirkaya-Budak, Sinem / Kabakci-Zorlu, Duygu / Turgut, Deniz / Kalay, Özem / Budak, Gungor / Narcı, Kübra / Arslan, Elif / Brown, Richard / Johnson, Ivan J / Dolgoborodov, Alexey / Semenyuk, Vladimir / Jain, Amit / Tetikol, H. Serhat / Jain, Varun / Ruehle, Mike / Lajoie, Bryan / Roddey, Cooper / Catreux, Severine / Mehio, Rami / Ahsan, Umair / Liu, Qian / Wang, Kai / Sahraeian, Sayed Mohammad Ebrahim / Fang, Li Tai / Mohiyuddin, Marghoob / Hung, Calvin / Jain, Chirag / Feng, Hanying / Li, Zhipan / Che, Luoqi / Sedlazeck, Fritz J. / Zook, Justin M.

    bioRxiv

    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 ... ...

    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.
    Keywords covid19
    Publisher BioRxiv
    Document type Article ; Online
    DOI 10.1101/2020.11.13.380741
    Database COVID19

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