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Your search keyword '"Darren C. Ames"' showing total 4 results
Start Over Author "Darren C. Ames" Topic 030104 developmental biology
4 results on '"Darren C. Ames"'

1. Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

Author

Ian B. Stanaway, Dan M. Roden, Divya Kalra, Dustin Key, Debra J. Abrams, David Fasel, Victor Castro, Brad Malin, Berta Almoguera, Beenish Riza, Meckenzie A. Behr, Eric Venner, Christine M. Eng, Joy Jayaseelan, Scott J. Hebbring, Michelle L. McGowan, Steven E. Scherer, Theresa L. Walunas, Mark Bowser, James D. Ralston, Wei-Qi Wei, Liwen Wang, David R. Murdock, Wayne H. Liang, Julia Wynn, Nancy D. Leslie, Laura J. Rasmussen-Torvik, Ming Ta (Michael) Lee, Frank D. Mentch, Lan Zhang, Alanna Kulchak Rahm, Josh F. Peterson, Jodell E. Linder, Joshua C. Smith, Soumitra Sengupta, Brendan J. Keating, Gina Vicente, Andrew Carroll, Nora B. Henrikson, Anne E. Justice, Heather S. Hain, Wen Liu, Andrea H. Ramirez, Matthew S. Lebo, Hana Zouk, Georgia L. Wiesner, Andrea L. Hartzler, Cassandra J. Pisieczko, Catherine M. Rives, Jessica Goehringer, Maegan V. Harden, John Lynch, Chiao-Feng Lin, Peter White, Phil Dunlea, Shawn N. Murphy, Mullai Murugan, Harshad Mahadeshwar, Mark Fleharty, Andrea Foster, Arvind Ramaprasan, Christopher A. Friedrich, Justin H. Gundelach, Hayley Lyon, Niall J. Lennon, Eric W. Klee, David R. Crosslin, Ge Zhang, Rongling Li, Ozan Dikilitas, Xiuping Liu, Christin Hoell, Aniwaa Owusu Obeng, Katherine D. Crew, Lisa M. Castillo, Justin Starren, Jonathan D. Mosley, Carrie L. Blout, Himanshu Sharma, Elizabeth M. McNally, Sarah T. Bland, Megan J. Puckelwartz, Matthew Varugheese, Keith Marsolo, Betty Woolf, Sharon Aufox, Janet L. Williams, Kimberly Walker, Murray H. Brilliant, Birgit Funke, Laura Allison Woods, Marylyn D. Ritchie, Brittany City, Todd Lingren, Hila Milo Rasouly, Lawrence J. Babb, Alex Fedotov, Robert C. Onofrio, Margaret Harr, Suzette J. Bielinski, Michael W. Wilson, Shubhabrata Mukherjee, Robert R. Freimuth, Chet Graham, Todd L. Edwards, Quinn S. Wells, Marc S. Williams, Jordan W. Smoller, Wendy K. Chung, Avni Santani, Paul K. Crane, George Hripcsak, QiPing Feng, Ali G. Gharavi, Yizhao Ni, Iftikhar J. Kullo, Michael Wagner, Philip E. Lammers, Michael J. Dinsmore, Thomas N. Person, Victoria Yi, Samuel E. Adunyah, Tim DeSmet, Eric B. Larson, Elizabeth Hynes, David C. Kochan, Eimear E. Kenny, Magalie S. Leduc, Lisa Mahanta, David Carrell, Paul S. Appelbaum, Viktoriya Korchina, Beth L. Cobb, Lynn Petukhova, Jessica De la Cruz, Patrick M. A. Sleiman, Stuart A. Scott, Tsung-Jung Wu, Gail P. Jarvik, Erwin P. Bottinger, Ken Wiley, Josh C. Denny, Melissa A. Basford, Samuel J. Aronson, David L. Veenstra, Yaping Yang, Kayla Marie Howell, John J. Connolly, Jessica Su, Yoonjung Yoonie Joo, Miguel Verbitsky, Sean M. Vargas, Cong Liu, Barbara Benoit, Andrew Hershey, Richard A. Gibbs, Cynthia A. Prows, Hana Bangash, Wendy Brodeur, Gauthami Chandanavelli, Sara L. Van Driest, Kurt D. Christensen, Elizabeth J. Bhoj, Vivian S. Gainer, Adam S. Gordon, Robert C. Green, Hakon Hakonarson, Krzysztof Kiryluk, Elisabeth A. Rosenthal, Rajbir Singh, James G. Linneman, Harrison Brand, Theodore Chiang, Sheila Dodge, Ingrid A. Holm, M. Geoffrey Hayes, Yunyun Jiang, Ning Shang, Samantha Baxter, Noralane M. Lindor, Kathleen A. Leppig, Teri A. Manolio, Sara E. Kalla, Pedro J. Caraballo, Ritika Raj, Aaron Scrol, Jyoti G. Dayal, Richard R. Sharp, Christie Kovar, Soumya Raychaudhuri, Sunghwan Sohn, Emily Kudalkar, Maddalena Marasa, Stacey Gabriel, Dan Schaid, Ladia Albertson-Junkans, Rex L. Chisholm, Maureen E. Smith, Donna M. Muzny, Casey Overby Taylor, Jianhong Hu, Elizabeth W. Karlson, Lisa Bastarache, Darren C. Ames, Joseph T. Glessner, Leora Witkowski, Siddharth Pratap, Qiaoyan Wang, Melissa A. Kelly, Adithya Balasubramanian, Kara Slowik, Terrie Kitchner, Barbara J. Klanderman, Shawn Denson, Mary Stroud, Alyssa Macbeth, Melanie F. Myers, Jesse Muniz, Kasia Tolwinski, Scott T. Weiss, Chunhua Weng, Stephanie M. Fullerton, John B. Harley, Christopher G. Chute, Heidi L. Rehm, Sheethal Jose, Andrew M. Glazer, Navya Shilpa Josyula, Kenneth M. Borthwick, Thomas E. Mullen, Mariza de Andrade, Leah C. Kottyan, Luke V. Rasmussen, James Meldrim, and Bahram Namjou

Subjects
0301 basic medicine, Standardization, Test data generation, business.industry, Computer science, Sequence Analysis, DNA, 030105 genetics & heredity, Precision medicine, Data science, Clinical decision support system, Biobank, Article, 3. Good health, Data sharing, 03 medical and health sciences, 030104 developmental biology, Genetics, Humans, Genetic Testing, Prospective Studies, Sample collection, Personalized medicine, Precision Medicine, business, Genetics (clinical)
Abstract

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Published
2019
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2. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

Author

Manisha Kher, Hyun Min Kang, Yossi Farjoun, Eric Banks, Allison A. Regier, Olga Krasheninina, Gonçalo R. Abecasis, Yeting Zhang, Tara C. Matise, Ira M. Hall, Daniel P. Howrigan, Jinchuan Xing, Will Salerno, Bo Juen Chen, Benjamin M. Neale, Adam C. English, David E. Larson, Heng Li, Michael C. Zody, and Darren C. Ames

Subjects
0301 basic medicine, Computer science, Science, General Physics and Astronomy, Computational biology, Genome, Article, General Biochemistry, Genetics and Molecular Biology, Statistical power, Bottleneck, DNA sequencing, Structural variation, 03 medical and health sciences, 0302 clinical medicine, Humans, Nucleotide, lcsh:Science, Indel, 030304 developmental biology, chemistry.chemical_classification, Whole genome sequencing, 0303 health sciences, Data processing, Multidisciplinary, Whole Genome Sequencing, Genome, Human, Scale (chemistry), Human Genetics, General Chemistry, Human genetics, 030104 developmental biology, chemistry, Genome Biology, lcsh:Q, 030217 neurology & neurosurgery
Abstract

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies., Sharing of whole genome sequencing (WGS) data improves study scale and power, but data from different groups are often incompatible. Here, US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

Published
2018

3. Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example

Author

Emily Beth Devine, Samuel J. Aronson, Andrea L. Hartzler, Keith Marsolo, Robert R. Freimuth, Casey Overby Taylor, Jamie R. Robinson, Pedro J. Caraballo, Luke V. Rasmussen, Darren C. Ames, Christopher G. Chute, Lawrence J. Babb, James D. Ralston, Shawn N. Murphy, Chunhua Weng, Iftikhar J. Kullo, Wayne H. Liang, Firas Wehbe, Ken Wiley, Richard A. Gibbs, Eric Venner, Marc S. Williams, John J. Connelly, and Josh F. Peterson

Subjects
0301 basic medicine, Knowledge management, Computer science, Health Informatics, Case Report, Letter of transmittal, Clinical decision support system, 03 medical and health sciences, Computer Communication Networks, Organizational boundaries, Genomic medicine, Electronic Health Records, Humans, Genetic Testing, Point of care, business.industry, Genome, Human, Information Dissemination, Genomics, Sequence Analysis, DNA, United States, Test (assessment), 030104 developmental biology, Work (electrical), business, Healthcare providers
Abstract

The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

Published
2017

4. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology

Author

Joshua C. Bis, Jennifer E. Huffman, Andrew D. Johnson, Colleen M. Sitlani, Susan R. Heckbert, Stephanie M. Gogarten, Andrew Carroll, Eric Boerwinkle, Braxton D. Mitchell, L. Adrienne Cupples, Alisa K. Manning, Cathy C. Laurie, James G. Wilson, Jeffrey R. O'Connell, Michael R. Brown, Xiaoming Liu, Stephen S. Rich, Matthew P. Conomos, Ramachandran S. Vasan, Joshua P. Lewis, Kenneth Rice, Stacey Gabriel, Alanna C. Morrison, Bruce M. Psaty, Nicholas L. Smith, Darren C. Ames, George J. Papanicolaou, Jerome I. Rotter, Cashell E. Jaquish, Namrata Gupta, William J Salerno, Achilleas N. Pitsillides, Jennifer A. Brody, and Richard A. Gibbs

Subjects
0301 basic medicine, Big Data, Big data, Medical and Health Sciences, Workflow, 0302 clinical medicine, 2.5 Research design and methodologies (aetiology), ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, Aetiology, Lung, education.field_of_study, Molecular Epidemiology, Genome, Environmental resource management, InformationSystems_DATABASEMANAGEMENT, Biological Sciences, Mobile Applications, Networking and Information Technology R&D (NITRD), Regression Analysis, TOPMed Hematology and Hemostasis Working Group, Biotechnology, education, Population, Information Dissemination, Biology, complex mixtures, CHARGE Analysis and Bioinformatics Working Group, Article, 03 medical and health sciences, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Genetics, Humans, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Molecular epidemiology, business.industry, Extramural, fungi, Human Genome, Fibrinogen, equipment and supplies, Data science, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Genetics, Population, Good Health and Well Being, Genetic epidemiology, bacteria, Generic health relevance, business, Commons, 030217 neurology & neurosurgery, Software, Developmental Biology
Abstract

The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multi-center WGS analyses, including data sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation, and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for transforming WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations.

Published
2017

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