Search

Your search keyword '"Silverstein, Kevin A. T."' showing total 217 results
Start Over Author "Silverstein, Kevin A. T."
217 results on '"Silverstein, Kevin A. T."'

1. Real-time Geoinformation Systems to Improve the Quality, Scalability, and Cost of Internet of Things for Agri-environment Research

Author

Runck, Bryan C., Schulz, Bobby, Bishop, Jeff, Carlson, Nathan, Chantigian, Bryan, Deters, Gary, Erdmann, Jesse, Ewing, Patrick M., Felzan, Michael, Fu, Xiao, Greyling, Jan, Hogan, Christopher J., Hollman, Andrew, Joglekar, Ali, Junker, Kris, Kantar, Michael, Kaunda, Lumbani, Krishna, Mohana, Lynch, Benjamin, Marchetto, Peter, Marsolek, Megan, McKay, Troy, Morris, Brad, Niaghi, Ali Rashid, Pamulaparthy, Keerthi, Pardey, Philip, Piotrowski, Ann, Poudyal, Christina, Prather, Tom, Raghavan, Barath, Reiter, Maggie, Rosen, Lucas, Salazar, Benjamin, Scobbie, Andrew, Sharma, Vasudha, Silverstein, Kevin A. T., Singh, Gurparteet, Strock, Jeff, Subedi, Samikshya, Tang, Evan, Turturillo, Gianna, Watkins, Eric, Webster, Blake, and Wilgenbusch, James

Subjects
Quantitative Biology - Quantitative Methods
Abstract

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices., Comment: 20 pages, 5 figures, 1 table

Published
2024

14. The genome of the zebra mussel, Dreissena polymorpha: a resource for comparative genomics, invasion genetics, and biocontrol

Author

McCartney, Michael A, primary, Auch, Benjamin, additional, Kono, Thomas, additional, Mallez, Sophie, additional, Zhang, Ying, additional, Obille, Angelico, additional, Becker, Aaron, additional, Abrahante, Juan E, additional, Garbe, John, additional, Badalamenti, Jonathan P, additional, Herman, Adam, additional, Mangelson, Hayley, additional, Liachko, Ivan, additional, Sullivan, Shawn, additional, Sone, Eli D, additional, Koren, Sergey, additional, Silverstein, Kevin A T, additional, Beckman, Kenneth B, additional, and Gohl, Daryl M, additional

Published
2021
Full Text
View/download PDF

15. A Transposon-Based Genetic Screen in Mice Identifies Genes Altered in Colorectal Cancer

Author

Starr, Timothy K., Allaei, Raha, Silverstein, Kevin A. T., Staggs, Rodney A., Sarver, Aaron L., Bergemann, Tracy L., Gupta, Mihir, O'Sullivan, M. Gerard, Matise, Ilze, Dupuy, Adam J., Collier, Lara S., Powers, Scott, Oberg, Ann L., Asmann, Yan W., Thibodeau, Stephen N., Tessarollo, Lino, Copeland, Neal G., Jenkins, Nancy A., Cormier, Robert T., and Largaespada, David A.

Published
2009
Full Text
View/download PDF

16. Digital agriculture platforms: Driving data‐enabled agricultural innovation in a world fraught with privacy and security concerns.

Author

Runck, Bryan C., Joglekar, Alison, Silverstein, Kevin A. T., Chan‐Kang, Connie, Pardey, Philip G., and Wilgenbusch, James C.

Abstract

Digital agriculture is often heralded as the next major wave of innovation in food and agriculture. Driven by big data, the intention is that digital agriculture will transform the entire research and development pipeline across agricultural value chains throughout the developed and developing worlds. Yet, issues concerning data quality, interoperability, intellectual property ownership and data privacy present considerable challenges to this vision. Digital agriculture platforms, which support data sharing, analysis, interoperability, and public and private sector collaboration, are one approach to address this challenge, but as a new research domain, there is a lack of conceptual clarity around what constitutes a "digital agriculture platform". Here, we use a "bottom up" and "top down" analysis approach to develop a taxonomy of the digital agriculture landscape. Then, we select a set of digital agriculture platforms for in‐depth analysis across a set of technical and use requirements. While digital agriculture will remain a constantly evolving landscape with varied technologies and application areas, this presents a first attempt to characterize this landscape and establish a common vocabulary for understanding digital agriculture platforms. Core Ideas: Digital agriculture is the next major wave of innovation in food and agriculture.Data quality, access, interoperability, and intellectual property present sizable challenges.Digital agriculture platforms are an enabling environment for innovation.The current field lacks clarity around the core elements that constitute a digital agriculture platform.We establish a common vocabulary for understanding digital agriculture platforms. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

19. The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma

Author

Pérez-Mancera, Pedro A., Rust, Alistair G., van der Weyden, Louise, Kristiansen, Glen, Li, Allen, Sarver, Aaron L., Silverstein, Kevin A. T., Grützmann, Robert, Aust, Daniela, Rümmele, Petra, Knösel, Thomas, Herd, Colin, Stemple, Derek L., Kettleborough, Ross, Brosnan, Jacqueline A., Li, Ang, Morgan, Richard, Knight, Spencer, Yu, Jun, Stegeman, Shane, Collier, Lara S., ten Hoeve, Jelle J., de Ridder, Jeroen, Klein, Alison P., Goggins, Michael, Hruban, Ralph H., Chang, David K., Biankin, Andrew V., Grimmond, Sean M., Johns, Amber L., Mawson, Amanda, Brancato, Mary-Anne L., Rowe, Sarah J., Simpson, Skye L., Martyn-Smith, Mona, Chantrill, Lorraine A., Chin, Venessa T., Chou, Angela, Cowley, Mark J., Humphris, Jeremy L., Jones, Marc D., Mead, Scott R., Nagrial, Adnan M., Pajic, Marina, Pettit, Jessica, Pinese, Mark, Rooman, Ilse, Wu, Jianmin, Daly, Roger J., Musgrove, Elizabeth A., Sutherland, Robert L., Waddell, Nicola, Kassahn, Karin S., Miller, David K., Wilson, Peter J., Patch, Ann-Marie, Song, Sarah, Harliwong, Ivon, Idrisoglu, Senel, Nourse, Craig, Nourbakhsh, Ehsan, Manning, Suzanne, Wani, Shivangi, Gongora, Milena, Anderson, Matthew, Holmes, Oliver, Leonard, Conrad, Taylor, Darrin, Wood, Scott, Xu, Christina, Nones, Katia, Fink, Lynn J., Christ, Angelika, Bruxner, Tim, Cloonan, Nicole, Newell, Felicity, Pearson, John V., Samra, Jaswinder S., Gill, Anthony J., Pavlakis, Nick, Guminski, Alex, Toon, Christopher, Blankin, Andrew V., Asghari, Ray, Merrett, Neil D., Pavey, Darren A., Das, Amithabad, Cosman, Peter H., Ismail, Kasim, O’Connor, Chelsie, Lam, Vincent W., McLeod, Duncan, Pleass, Henry C., James, Virginia, Kench, James G., Cooper, Caroline L., Joseph, David, Sandroussi, Charbel, Crawford, Michael, Texler, Michael, Forrest, Cindy, Laycock, Andrew, Epari, Krishna P., Ballal, Mo, Fletcher, David R., Mukhedkar, Sanjay, Spry, Nigel A., DeBoer, Bastiaan, Chai, Ming, Feeney, Kynan, Zeps, Nikolajs, Beilin, Maria, Nguyen, Nam Q., Ruszkiewicz, Andrew R., Worthley, Chris, Tan, Chuan P., Debrencini, Tamara, Chen, John, Brooke-Smith, Mark E., Papangelis, Virginia, Tang, Henry, Barbour, Andrew P., Clouston, Andrew D., Martin, Patrick, O’Rourke, Thomas J., Chiang, Amy, Fawcett, Jonathan W., Slater, Kellee, Yeung, Shinn, Hatzifotis, Michael, Hodgkinson, Peter, Christophi, Christopher, Nikfarjam, Mehrdad, Biobank, Victorian Cancer, Eshleman, James R., Maitra, Anirban, Iacobuzio-Donahue, Christine A., Schulick, Richard D., Wolfgang, Christopher L., Morgan, Richard A., Lawlor, Rita T., Beghelli, Stefania, Corbo, Vincenzo, Scardoni, Maria, Bassi, Claudio, Tempero, Margaret A., Wessels, Lodewyk F. A., Wood, Stephen A., Pilarsky, Christian, Largaespada, David A., Adams, David J., and Tuveson, David A.

Published
2012
Full Text
View/download PDF

21. The Medicago genome provides insight into the evolution of rhizobial symbioses

Author

Young, Nevin D., Debellé, Frédéric, Oldroyd, Giles E. D., Geurts, Rene, Cannon, Steven B., Udvardi, Michael K., Benedito, Vagner A., Mayer, Klaus F. X., Gouzy, Jérôme, Schoof, Heiko, Van de Peer, Yves, Proost, Sebastian, Cook, Douglas R., Meyers, Blake C., Spannagl, Manuel, Cheung, Foo, De Mita, Stéphane, Krishnakumar, Vivek, Gundlach, Heidrun, Zhou, Shiguo, Mudge, Joann, Bharti, Arvind K., Murray, Jeremy D., Naoumkina, Marina A., Rosen, Benjamin, Silverstein, Kevin A. T., Tang, Haibao, Rombauts, Stephane, Zhao, Patrick X., Zhou, Peng, Barbe, Valérie, Bardou, Philippe, Bechner, Michael, Bellec, Arnaud, Berger, Anne, Bergès, Hélène, Bidwell, Shelby, Bisseling, Ton, Choisne, Nathalie, Couloux, Arnaud, Denny, Roxanne, Deshpande, Shweta, Dai, Xinbin, Doyle, Jeff J., Dudez, Anne-Marie, Farmer, Andrew D., Fouteau, Stéphanie, Franken, Carolien, Gibelin, Chrystel, Gish, John, Goldstein, Steven, González, Alvaro J., Green, Pamela J., Hallab, Asis, Hartog, Marijke, Hua, Axin, Humphray, Sean J., Jeong, Dong-Hoon, Jing, Yi, Jöcker, Anika, Kenton, Steve M., Kim, Dong-Jin, Klee, Kathrin, Lai, Hongshing, Lang, Chunting, Lin, Shaoping, Macmil, Simone L., Magdelenat, Ghislaine, Matthews, Lucy, McCorrison, Jamison, Monaghan, Erin L., Mun, Jeong-Hwan, Najar, Fares Z., Nicholson, Christine, Noirot, Céline, O’Bleness, Majesta, Paule, Charles R., Poulain, Julie, Prion, Florent, Qin, Baifang, Qu, Chunmei, Retzel, Ernest F., Riddle, Claire, Sallet, Erika, Samain, Sylvie, Samson, Nicolas, Sanders, Iryna, Saurat, Olivier, Scarpelli, Claude, Schiex, Thomas, Segurens, Béatrice, Severin, Andrew J., Sherrier, Janine D., Shi, Ruihua, Sims, Sarah, Singer, Susan R., Sinharoy, Senjuti, Sterck, Lieven, Viollet, Agnès, Wang, Bing-Bing, Wang, Keqin, Wang, Mingyi, Wang, Xiaohong, Warfsmann, Jens, Weissenbach, Jean, White, Doug D., White, Jim D., Wiley, Graham B., Wincker, Patrick, Xing, Yanbo, Yang, Limei, Yao, Ziyun, Ying, Fu, Zhai, Jixian, Zhou, Liping, Zuber, Antoine, Dénarié, Jean, Dixon, Richard A., May, Gregory D., Schwartz, David C., Rogers, Jane, Quétier, Francis, Town, Christopher D., and Roe, Bruce A.

Published
2011
Full Text
View/download PDF

23. Evolution of pathogenicity and sexual reproduction in eight Candida genomes

Author

Butler, Geraldine, Rasmussen, Matthew D., Lin, Michael F., Santos, Manuel A. S., Sakthikumar, Sharadha, Munro, Carol A., Rheinbay, Esther, Grabherr, Manfred, Forche, Anja, Reedy, Jennifer L., Agrafioti, Ino, Arnaud, Martha B., Bates, Steven, Brown, Alistair J. P., Brunke, Sascha, Costanzo, Maria C., Fitzpatrick, David A., de Groot, Piet W. J., Harris, David, Hoyer, Lois L., Hube, Bernhard, Klis, Frans M., Kodira, Chinnappa, Lennard, Nicola, Logue, Mary E., Martin, Ronny, Neiman, Aaron M., Nikolaou, Elissavet, Quail, Michael A., Quinn, Janet, Santos, Maria C., Schmitzberger, Florian F., Sherlock, Gavin, Shah, Prachi, Silverstein, Kevin A. T., Skrzypek, Marek S., Soll, David, Staggs, Rodney, Stansfield, Ian, Stumpf, Michael P. H., Sudbery, Peter E., Srikantha, Thyagarajan, Zeng, Qiandong, Berman, Judith, Berriman, Matthew, Heitman, Joseph, Gow, Neil A. R., Lorenz, Michael C., Birren, Bruce W., Kellis, Manolis, and Cuomo, Christina A.

Published
2009
Full Text
View/download PDF

27. TAPDANCE: An automated tool to identify and annotate transposon insertion CISs and associations between CISs from next generation sequence data

Author

Sarver Aaron L, Erdman Jesse, Starr Tim, Largaespada David A, and Silverstein Kevin A T

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Next generation sequencing approaches applied to the analyses of transposon insertion junction fragments generated in high throughput forward genetic screens has created the need for clear informatics and statistical approaches to deal with the massive amount of data currently being generated. Previous approaches utilized to 1) map junction fragments within the genome and 2) identify Common Insertion Sites (CISs) within the genome are not practical due to the volume of data generated by current sequencing technologies. Previous approaches applied to this problem also required significant manual annotation. Results We describe Transposon Annotation Poisson Distribution Association Network Connectivity Environment (TAPDANCE) software, which automates the identification of CISs within transposon junction fragment insertion data. Starting with barcoded sequence data, the software identifies and trims sequences and maps putative genomic sequence to a reference genome using the bowtie short read mapper. Poisson distribution statistics are then applied to assess and rank genomic regions showing significant enrichment for transposon insertion. Novel methods of counting insertions are used to ensure that the results presented have the expected characteristics of informative CISs. A persistent mySQL database is generated and utilized to keep track of sequences, mappings and common insertion sites. Additionally, associations between phenotypes and CISs are also identified using Fisher’s exact test with multiple testing correction. In a case study using previously published data we show that the TAPDANCE software identifies CISs as previously described, prioritizes them based on p-value, allows holistic visualization of the data within genome browser software and identifies relationships present in the structure of the data. Conclusions The TAPDANCE process is fully automated, performs similarly to previous labor intensive approaches, provides consistent results at a wide range of sequence sampling depth, has the capability of handling extremely large datasets, enables meaningful comparison across datasets and enables large scale meta-analyses of junction fragment data. The TAPDANCE software will greatly enhance our ability to analyze these datasets in order to increase our understanding of the genetic basis of cancers.

Published
2012
Full Text
View/download PDF

28. G2F NIFA FACT Workshop: High Throughput, Field-based Phenotyping Technologies for the Genomes to Fields (G2F) Initiative

Author

Lawrence-Dill, Carolyn J., Schnable, Patrick S., Springer, Nathan M., Leon, Natalia De, Jode W. Edwards, Ertl, David, Kaeppler, Shawn M., Lauter, Nick, McKay, John K., Munoz-Arriola, Francisco, Murray, Seth C., Pauli, Duke, Cruzato, Nathalia Penna, Ratcliff, Colby, Schnable, James C., Silverstein, Kevin A. T., Spalding, Edgar P., Thompson, Addie, Swanson-Wagner, Ruth A., Wallace, Jason, Walley, Justin W., and Jianming Yu

Abstract

The U.S. has long played a leading role in developing advanced agricultural technology. We must continue to develop novel approaches to increase the production of food, fiber and fuel while protecting our natural resources and ensuring an economically vibrant agriculture sector. The Genomes to Fields (G2F) Initiative strives to take advantage of new technologies to improve the productivity and stability of maize. In particular, this initiative seeks to connect advances in our understanding of crop genomes with new robotics, high-throughput sensing technologies, and other data gathering devices to understand how plant traits are influenced by genetics and the environment with a long-term aim of developing crops that will exhibit sustainable enhanced productivity across diverse sets of environments and years.In January 2018, G2F hosted a meeting in Ames, Iowa to consider trajectories for research in field-based phenotyping and how best to support those trajectories, and informing USDA NIFA and other federal agencies of our findings and conclusions. For four years, a distributed network of G2F field sites and public-sector collaborators has been generating the data needed to develop predictive models. Generating a vibrant community of researchers from diverse disciplines including the breadth of the plant sciences (e.g., genetics, agronomy, physiology, modeling, and breeding), engineering, computational sciences, and climatology is critical to the full success of the initiative. Many topics were discussed over the course of the three-day workshop, but four themes emerged as areas ripe for focused effort in the coming years: (1) support for ongoing community experiments; (2) development and use of field sensors and plant imaging platforms, (3) creation of data management, sharing, and analytics platforms especially for making effective use of large scale image sets, and (4) engaging additional scientific disciplines in G2F and training the next generation of agricultural scientists. It was suggested that these topics were of interest to advance not only the goals of the G2F initiative, but also the field of predictive plant phenomics more generally. The group supported the idea to enhance coordination efforts across all four themes by designating and/or creating a number of (5) High-Intensity Phenotyping Sites (HIPS) where individualized research areas and local expertise could develop alongside intentional, coordinated linkages focused on advancing topics within the four shared themes. While community experiments are necessarily extensive, these HIPS experiments can be embedded within the G2F testing network and allow for more intensive investigation and development of predictive phenomics tools. Tools and learnings from the HIPS could then be deployed more broadly.

Published
2018
Full Text
View/download PDF

29. The Genome of the Zebra Mussel,Dreissena polymorpha: A Resource for Invasive Species Research

Author

McCartney, Michael A., primary, Auch, Benjamin, additional, Kono, Thomas, additional, Mallez, Sophie, additional, Zhang, Ying, additional, Obille, Angelico, additional, Becker, Aaron, additional, Abrahante, Juan E., additional, Garbe, John, additional, Badalamenti, Jonathan P., additional, Herman, Adam, additional, Mangelson, Hayley, additional, Liachko, Ivan, additional, Sullivan, Shawn, additional, Sone, Eli D., additional, Koren, Sergey, additional, Silverstein, Kevin A. T., additional, Beckman, Kenneth B., additional, and Gohl, Daryl M., additional

Published
2019
Full Text
View/download PDF

32. Strategies for optimizing BioNano and Dovetail explored through a second reference quality assembly for the legume model, Medicago truncatula

Author

Moll, Karen M., primary, Zhou, Peng, additional, Ramaraj, Thiruvarangan, additional, Fajardo, Diego, additional, Devitt, Nicholas P., additional, Sadowsky, Michael J., additional, Stupar, Robert M., additional, Tiffin, Peter, additional, Miller, Jason R., additional, Young, Nevin D., additional, Silverstein, Kevin A. T., additional, and Mudge, Joann, additional

Published
2017
Full Text
View/download PDF

33. Hybrid assembly with long and short reads improves discovery of gene family expansions

Author

Miller, Jason R., primary, Zhou, Peng, additional, Mudge, Joann, additional, Gurtowski, James, additional, Lee, Hayan, additional, Ramaraj, Thiruvarangan, additional, Walenz, Brian P., additional, Liu, Junqi, additional, Stupar, Robert M., additional, Denny, Roxanne, additional, Song, Li, additional, Singh, Namrata, additional, Maron, Lyza G., additional, McCouch, Susan R., additional, McCombie, W. Richard, additional, Schatz, Michael C., additional, Tiffin, Peter, additional, Young, Nevin D., additional, and Silverstein, Kevin A. T., additional

Published
2017
Full Text
View/download PDF

34. Exploring structural variation and gene family architecture with De Novo assemblies of 15 Medicago genomes

Author

Zhou, Peng, primary, Silverstein, Kevin A. T., additional, Ramaraj, Thiruvarangan, additional, Guhlin, Joseph, additional, Denny, Roxanne, additional, Liu, Junqi, additional, Farmer, Andrew D., additional, Steele, Kelly P., additional, Stupar, Robert M., additional, Miller, Jason R., additional, Tiffin, Peter, additional, Mudge, Joann, additional, and Young, Nevin D., additional

Published
2017
Full Text
View/download PDF

35. Molecular model of hydrophobic solvation.

Author

Silverstein, Kevin A. T., Haymet, A. D. J., and Dill, Ken A.

Subjects
*HYDROPHOBIC surfaces, *MOLECULES, *HYDROGEN bonding
Abstract

Studies the physical basis for the hydrophobic effect using a simple statistical mechanical model of water in which water molecules are represented as Lennard-Jones disks with hydrogen bonding arms. Key to hydrophobic thermodynamics; Interpreting molecular model simulations using the Muller framework.

Published
1999
Full Text
View/download PDF

36. Augmenting Chinese hamster genome assembly by identifying regions of high confidence

Author

Vishwanathan, Nandita, primary, Bandyopadhyay, Arpan A., additional, Fu, Hsu‐Yuan, additional, Sharma, Mohit, additional, Johnson, Kathryn C., additional, Mudge, Joann, additional, Ramaraj, Thiruvarangan, additional, Onsongo, Getiria, additional, Silverstein, Kevin A. T., additional, Jacob, Nitya M., additional, Le, Huong, additional, Karypis, George, additional, and Hu, Wei‐Shou, additional

Published
2016
Full Text
View/download PDF

38. Clonal Dissemination of Enterobacter cloacae Harboring bla KPC-3 in the Upper Midwestern United States

Author

Hargreaves, Melissa L., primary, Shaw, Kristin M., additional, Dobbins, Ginette, additional, Snippes Vagnone, Paula M., additional, Harper, Jane E., additional, Boxrud, Dave, additional, Lynfield, Ruth, additional, Aziz, Maliha, additional, Price, Lance B., additional, Silverstein, Kevin A. T., additional, Danzeisen, Jessica L., additional, Youmans, Bonnie, additional, Case, Kyle, additional, Sreevatsan, Srinand, additional, and Johnson, Timothy J., additional

Published
2015
Full Text
View/download PDF

40. Two QTL characterized for soft scald and soggy breakdown in apple ( Malus × domestica) through pedigree-based analysis of a large population of interconnected families.

Author

Howard, Nicholas P., van de Weg, Eric, Tillman, John, Tong, Cindy B. S., Silverstein, Kevin A. T., and Luby, James J.

Subjects
APPLES, MOLECULAR biology, HAPLOTYPES, CULTIVARS, PLANTS, BIOMARKERS
Abstract

Soft scald and soggy breakdown are important postharvest physiological disorders of apple ( Malus × domestica). 'Honeycrisp' and some of its offspring are particularly susceptible to developing these disorders. The purpose of this study was to identify molecular markers associated with high incidences of soft scald and soggy breakdown for use in marker-assisted breeding. Towards this aim, we employed a pedigree-based approach using mostly germplasm related to 'Honeycrisp.' Two quantitative trait loci (QTL) were consistently identified on linkage groups (LGs) 2 and 16 across the 2014 and 2015 harvest years. The same QTL were identified for both storage disorders, indicating that they may be physiologically related. 'Honeycrisp' is homozygous for an identical by state haplotype at the LG2 QTL that was consistently associated with a deleterious effect on soft scald and soggy breakdown incidence. This haplotype was traced through SNP-confirmed pedigrees to the following cultivars: 'Grimes Golden,' 'Northern Spy,' 'Rome Beauty,' and 'Fireside' and is common in derived apple germplasm. Haplotypes at the LG16 QTL could not be adequately characterized due to variation between years combined with effects of this QTL being of relatively smaller size and being most evident in individuals that carry two copies of the deleterious haplotype at the LG2 QTL. These results suggest that limiting homozygosity of the deleterious haplotype at the LG2 QTL through marker-assisted breeding would be a valid strategy to limit soft scald and soggy breakdown incidences in apple seedling populations. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

41. Clinical Validation of Targeted Next-Generation Sequencing for Inherited Disorders

Author

Yohe, Sophia, primary, Hauge, Adam, additional, Bunjer, Kari, additional, Kemmer, Teresa, additional, Bower, Matthew, additional, Schomaker, Matthew, additional, Onsongo, Getiria, additional, Wilson, Jon, additional, Erdmann, Jesse, additional, Zhou, Yi, additional, Deshpande, Archana, additional, Spears, Michael D., additional, Beckman, Kenneth, additional, Silverstein, Kevin A. T., additional, and Thyagarajan, Bharat, additional

Published
2015
Full Text
View/download PDF

43. Exploring structural variation and gene family architecture with De Novo assemblies of 15 Medicago genomes.

Author

Peng Zhou, Silverstein, Kevin A. T., Ramaraj, Thiruvarangan, Guhlin, Joseph, Denny, Roxanne, Junqi Liu, Farmer, Andrew D., Steele, Kelly P., Stupar, Robert M., Miller, Jason R., Tiffin, Peter, Mudge, Joann, and Young, Nevin D.

Subjects
*MEDICAGO truncatula, *GENE families, *NUCLEOTIDE sequencing, *PLANT genomes, *DNA copy number variations
Abstract

Background: Previous studies exploring sequence variation in the model legume, Medicago truncatula, relied on mapping short reads to a single reference. However, read-mapping approaches are inadequate to examine large, diverse gene families or to probe variation in repeat-rich or highly divergent genome regions. De novo sequencing and assembly of M. truncatula genomes enables near-comprehensive discovery of structural variants (SVs), analysis of rapidly evolving gene families, and ultimately, construction of a pan-genome. Results: Genome-wide synteny based on 15 de novo M. truncatula assemblies effectively detected different types of SVs indicating that as much as 22% of the genome is involved in large structural changes, altogether affecting 28% of gene models. A total of 63 million base pairs (Mbp) of novel sequence was discovered, expanding the reference genome space for Medicago by 16%. Pan-genome analysis revealed that 42% (180 Mbp) of genomic sequences is missing in one or more accession, while examination of de novo annotated genes identified 67% (50,700) of all ortholog groups as dispensable - estimates comparable to recent studies in rice, maize and soybean. Rapidly evolving gene families typically associated with biotic interactions and stress response were found to be enriched in the accession-specific gene pool. The nucleotide-binding site leucine-rich repeat (NBS-LRR) family, in particular, harbors the highest level of nucleotide diversity, large effect single nucleotide change, protein diversity, and presence/absence variation. However, the leucine-rich repeat (LRR) and heat shock gene families are disproportionately affected by large effect single nucleotide changes and even higher levels of copy number variation. Conclusions: Analysis of multiple M. truncatula genomes illustrates the value of de novo assemblies to discover and describe structural variation, something that is often under-estimated when using read-mapping approaches. Comparisons among the de novo assemblies also indicate that different large gene families differ in the architecture of their structural variation. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

48. Erratum: Corrigendum: The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma

Author

Pérez-Mancera, Pedro A., primary, Rust, Alistair G., additional, van der Weyden, Louise, additional, Kristiansen, Glen, additional, Li, Allen, additional, Sarver, Aaron L., additional, Silverstein, Kevin A. T., additional, Grützmann, Robert, additional, Aust, Daniela, additional, Rümmele, Petra, additional, Knösel, Thomas, additional, Herd, Colin, additional, Stemple, Derek L., additional, Kettleborough, Ross, additional, Brosnan, Jacqueline A., additional, Li, Ang, additional, Morgan, Richard, additional, Knight, Spencer, additional, Yu, Jun, additional, Stegeman, Shane, additional, Collier, Lara S., additional, ten Hoeve, Jelle J., additional, de Ridder, Jeroen, additional, Klein, Alison P., additional, Goggins, Michael, additional, Hruban, Ralph H., additional, Chang, David K., additional, Biankin, Andrew V., additional, Grimmond, Sean M., additional, Initiative, Australian Pancreatic Cancer Genome, additional, Wessels, Lodewyk F. A., additional, Wood, Stephen A., additional, Iacobuzio-Donahue, Christine A., additional, Pilarsky, Christian, additional, Largaespada, David A., additional, Adams, David J., additional, and Tuveson, David A., additional

Published
2013
Full Text
View/download PDF

49. ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and assembly de novo.

Author

Yang, Rendong, Nelson, Andrew C., Henzler, Christine, Thyagarajan, Bharat, and Silverstein, Kevin A. T.

Subjects
NUCLEOTIDE sequencing, HUMAN genome, DNA analysis, HUMAN chromosomes, HUMAN genetics
Abstract

Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel's superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel. [ABSTRACT FROM AUTHOR]

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results