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241 results on '"Feltus, F. Alex"'

2. Hydra -- A Federated Data Repository over NDN

Author

Presley, Justin, Wang, Xi, Brandel, Tym, Ai, Xusheng, Podder, Proyash, Yu, Tianyuan, Patil, Varun, Zhang, Lixia, Afanasyev, Alex, Feltus, F. Alex, and Shannigrahi, Susmit

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Today's big data science communities manage their data publication and replication at the application layer. These communities utilize myriad mechanisms to publish, discover, and retrieve datasets - the result is an ecosystem of either centralized, or otherwise a collection of ad-hoc data repositories. Publishing datasets to centralized repositories can be process-intensive, and those repositories do not accept all datasets. The ad-hoc repositories are difficult to find and utilize due to differences in data names, metadata standards, and access methods. To address the problem of scientific data publication and storage, we have designed Hydra, a secure, distributed, and decentralized data repository made of a loose federation of storage servers (nodes) provided by user communities. Hydra runs over Named Data Networking (NDN) and utilizes the State Vector Sync (SVS) protocol that lets individual nodes maintain a "global view" of the system. Hydra provides a scalable and resilient data retrieval service, with data distribution scalability achieved via NDN's built-in data anycast and in-network caching and resiliency against individual server failures through automated failure detection and maintaining a specific degree of replication. Hydra utilizes "Favor", a locally calculated numerical value to decide which nodes will replicate a file. Finally, Hydra utilizes data-centric security for data publication and node authentication. Hydra uses a Network Operation Center (NOC) to bootstrap trust in Hydra nodes and data publishers. The NOC distributes user and node certificates and performs the proof-of-possession challenges. This technical report serves as the reference for Hydra. It outlines the design decisions, the rationale behind them, the functional modules, and the protocol specifications.

Published
2022

3. NEANDERTAL DNA AND THE HUMAN MIND.

Author

CASANOVA, EMILY L. and FELTUS, F. ALEX

Subjects
*LOCUS (Genetics), *HEREDITY, *GENE expression, *CROHN'S disease, *GENETIC variation
Abstract

The article from Scientific American explores the impact of Neandertal DNA on modern human cognition and health. Researchers have found evidence that Neandertals had cognitive abilities similar to Homo sapiens, and interbreeding between the two species has left a genetic fingerprint in modern humans. Neandertal DNA affects various aspects of modern human physiology, including immune disorders, skin color, and brain development, potentially influencing conditions such as autism. Ongoing research aims to uncover the intricate ways in which Neandertal DNA shapes our genetic makeup and its implications for neurodevelopmental conditions like autism. [Extracted from the article]

Published
2025
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4. AABAC -- Automated Attribute Based Access Control for Genomics Data

Author

Reddick, David, Presley, Justin, Feltus, F. Alex, and Shannigrahi, Susmit

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

The COVID-19 crisis has demonstrated the potential of cutting-edge genomics research. However, privacy of these sensitive pieces of information is an area of significant concern for genomics researchers. The current security models makes it difficult to create flexible and automated data sharing frameworks. These models also increases the complexity of adding or revoking access without contacting the data publisher. In this work, we investigate an automated attribute-based access control (AABAC) model for genomics data over Named Data Networking (NDN). AABAC secures the data itself rather than the storage location or transmission channel, provides automated data invalidation, and automates key retrieval and data validation while maintaining the ability to control access. We show that AABC when combined with NDN provide a secure and flexible combination for work with genomics research., Comment: Named Data Networking, Access Control, Genomics Data

Published
2022

5. Cellular State Transformations using Generative Adversarial Networks

Author

Targonski, Colin, Shealy, Benjamin T., Smith, Melissa C., and Feltus, F. Alex

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

We introduce a novel method to unite deep learning with biology by which generative adversarial networks (GANs) generate transcriptome perturbations and reveal condition-defining gene expression patterns. We find that a generator conditioned to perturb any input gene expression profile simulates a realistic transition between source and target RNA expression states. The perturbed samples follow a similar distribution to original samples from the dataset, also suggesting these are biologically meaningful perturbations. Finally, we show that it is possible to identify the genes most positively and negatively perturbed by the generator and that the enriched biological function of the perturbed genes are realistic. We call the framework the Transcriptome State Perturbation Generator (TSPG), which is open source software available at https://github.com/ctargon/TSPG., Comment: 11 pages, 5 figures

Published
2019

11. Effectively integrating information content and structural relationship to improve the GO-based similarity measure between proteins

Author

Li, Bo, Wang, James Z., Feltus, F. Alex, Zhou, Jizhong, and Luo, Feng

Subjects
Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Genomics
Abstract

The Gene Ontology (GO) provides a knowledge base to effectively describe proteins. However, measuring similarity between proteins based on GO remains a challenge. In this paper, we propose a new similarity measure, information coefficient similarity measure (SimIC), to effectively integrate both the information content (IC) of GO terms and the structural information of GO hierarchy to determine the similarity between proteins. Testing on yeast proteins, our results show that SimIC efficiently addresses the shallow annotation issue in GO, thus improves the correlations between GO similarities of yeast proteins and their expression similarities as well as between GO similarities of yeast proteins and their sequence similarities. Furthermore, we demonstrate that the proposed SimIC is superior in predicting yeast protein interactions. We predict 20484 yeast protein-protein interactions (PPIs) between 2462 proteins based on the high SimIC values of biological process (BP) and cellular component (CC). Examining the 214 MIPS complexes in our predicted PPIs shows that all members of 159 MIPS complexes can be found in our PPI predictions, which is more than those (120/214) found in PPIs predicted by relative specificity similarity (RSS). Integrating IC and structural information of GO hierarchy can improve the effectiveness of the semantic similarity measure of GO terms. The new SimIC can effectively correct the effect of shallow annotation, and then provide an effective way to measure similarity between proteins based on Gene Ontology.

Published
2010

12. Laser Capture Microdissection Transcriptome Reveals Spatiotemporal Tissue Gene Expression Patterns of Medicago truncatula Roots Responding to Rhizobia

Author

Schnabel, Elise, primary, Thomas, Jacklyn, additional, El-Hawaz, Rabia, additional, Gao, Yueyao, additional, Poehlman, William L., additional, Chavan, Suchitra, additional, Pasha, Asher, additional, Esteban, Eddi, additional, Provart, Nicholas, additional, Feltus, F. Alex, additional, and Frugoli, Julia, additional

Published
2023
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14. The Sorghum bicolor genome and the diversification of grasses

Author

Paterson, Andrew H., Bowers, John E., Bruggmann, Remy, dubchak, Inna, Grimwood, Jane, Gundlach, Heidrun, Haberer, Georg, Hellsten, Uffe, Mitros, Therese, Poliakov, Alexander, Schmutz, Jeremy, Spannagl, Manuel, Tang, Haibo, Wang, Xiyin, Wicker, Thomas, Bharti, Arvind K., Chapman, Jarrod, Feltus, F. Alex, Gowik, Udo, Grigoriev, Igor V., Lyons, Eric, Maher, Christopher A., Martis, Mihaela, Marechania, Apurva, Otillar, Robert P., Penning, Bryan W., Salamov, Asaf. A., Wang, Yu, Zhang, Lifang, Carpita, Nicholas C., Freeling, Michael, Gingle, Alan R., hash, C. Thomas, Keller, Beat, Klein, Patricia, Kresovich, Stephen, McCann, Maureen C., Ming, Ray, Peterson, Daniel G., ur-Rahman, Mehboob-, Ware, Doreen, Westhoff, Peter, Mayer, Klaus F. X., Messing, Joachim, and Rokhsar, Daniel S.

Abstract

Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the approx730-megabase Sorghum bicolor (L.) Moench genome, placing approx98percent of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the approx75percent larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization approx70 million years ago, most duplicated gene sets lost one member before the sorghum rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24percent of genes are grass-specific and 7percent are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum's drought tolerance.

Published
2009

18. Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format

Author

Mutsuddy, Arnab, Erdem, Cemal, Huggins, Jonah R, Salim, Misha, Cook, Daniel, Hobbs, Nicole, Feltus, F Alex, Birtwistle, Marc R, Mutsuddy, Arnab, Erdem, Cemal, Huggins, Jonah R, Salim, Misha, Cook, Daniel, Hobbs, Nicole, Feltus, F Alex, and Birtwistle, Marc R

Abstract

Summary: Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Availability and implementation: Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).

Published
2023
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21. Transcriptome analysis ofMedicago truncatulaAutoregulation of Nodulation mutants reveals that disruption of the SUNN pathway causes constitutive expression changes in a small group of genes, but the overall response to rhizobia resembles wild type, including induction ofTML1andTML2

Author

Schnabel, Elise L., primary, Chavan, Suchitra A., additional, Gao, Yueyao, additional, Poehlman, William L., additional, Feltus, F. Alex, additional, and Frugoli, Julia A., additional

Published
2023
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27. Sequencing papaya X and Y h chromosomes reveals molecular basis of incipient sex chromosome evolution

Author

Wang, Jianping, Na, Jong-Kuk, Yu, Qingyi, Gschwend, Andrea R., Han, Jennifer, Zeng, Fanchang, Aryal, Rishi, VanBuren, Robert, Murray, Jan E., Zhang, Wenli, Navajas-Pérez, Rafael, Feltus, F. Alex, Lemke, Cornelia, Tong, Eric J., Chen, Cuixia, Wai, Ching Man, Singh, Ratnesh, Wang, Ming-Li, Min, Xiang Jia, Alam, Maqsudul, Charlesworth, Deborah, Moore, Paul H., Jiang, Jiming, Paterson, Andrew H., and Ming, Ray

Published
2012

35. Addressing noise in co-expression network construction

Author

Burns, Joshua J R, primary, Shealy, Benjamin T, additional, Greer, Mitchell S, additional, Hadish, John A, additional, McGowan, Matthew T, additional, Biggs, Tyler, additional, Smith, Melissa C, additional, Feltus, F Alex, additional, and Ficklin, Stephen P, additional

Published
2021
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40. Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development

Author

Rong, Junkang, Feltus, F. Alex, Waghmare, Vijay N., Pierce, Gary J., Chee, Peng W., Draye, Xavier, Saranga, Yehoshua, Wright, Robert J., Wilkins, Thea A., May, O. Lloyd, Smith, C. Wayne, Gannaway, John R., Wendel, Jonathan F., and Paterson, Andrew H.

Subjects
Fibers -- Analysis, Quantitative trait loci -- Evaluation, Chromosome mapping -- Evaluation, Cotton -- Evaluation, Meta-analysis -- Usage, Clusters (Chemistry) -- Evaluation, Clusters (Chemistry) -- Genetic aspects, Biological sciences
Abstract

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiber-producing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks.

Published
2007

44. Negotiated Sharing of Pandemic Data, Models, and Resources

Author

Cutcher‐Gershenfeld, Joel, primary, Baker, Karen S., additional, Berente, Nicholas, additional, Berkman, Paul Arthur, additional, Canavan, Pat, additional, Feltus, F. Alex, additional, Garmulewicz, Alysia, additional, Hutchins, Ron, additional, King, John Leslie, additional, Kirkpatrick, Christine, additional, Lenhardt, Chris, additional, Lewis, Spencer, additional, Maffe, Michael, additional, Mittleman, Barbara, additional, Sampath, Rajesh, additional, Shin, Namchul, additional, Stall, Shelley, additional, Winter, Susan, additional, and Veazey, Pips, additional

Published
2020
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46. The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus)

Author

Ming, Ray, Hou, Shaobin, Feng, Yun, Yu, Qingyi, Dionne-Laporte, Alexandre, Saw, Jimmy H., Senin, Pavel, Wang, Wei, Ly, Benjamin V., Lewis, Kanako L. T., Salzberg, Steven L., Feng, Lu, Jones, Meghan R., Skelton, Rachel L., Murray, Jan E., Chen, Cuixia, Qian, Wubin, Shen, Junguo, Du, Peng, Eustice, Moriah, Tong, Eric, Tang, Haibao, Lyons, Eric, Paull, Robert E., Michael, Todd P., Wall, Kerr, Rice, Danny W., Albert, Henrik, Wang, Ming-Li, Zhu, Yun J., Schatz, Michael, Nagarajan, Niranjan, Acob, Ricelle A., Guan, Peizhu, Blas, Andrea, Wai, Ching Man, Ackerman, Christine M., Ren, Yan, Liu, Chao, Wang, Jianmei, Wang, Jianping, Na, Jong-Kuk, Shakirov, Eugene V., Haas, Brian, Thimmapuram, Jyothi, Nelson, David, Wang, Xiyin, Bowers, John E., Gschwend, Andrea R., Delcher, Arthur L., Singh, Ratnesh, Suzuki, Jon Y., Tripathi, Savarni, Neupane, Kabi, Wei, Hairong, Irikura, Beth, Paidi, Maya, Jiang, Ning, Zhang, Wenli, Presting, Gernot, Windsor, Aaron, Navajas-Pérez, Rafael, Torres, Manuel J., Feltus, F. Alex, Porter, Brad, Li, Yingjun, Burroughs, A. Max, Luo, Ming-Cheng, Liu, Lei, Christopher, David A., Mount, Stephen M., Moore, Paul H., Sugimura, Tak, Jiang, Jiming, Schuler, Mary A., Friedman, Vikki, Mitchell-Olds, Thomas, Shippen, Dorothy E., dePamphilis, Claude W., Palmer, Jeffrey D., Freeling, Michael, Paterson, Andrew H., Gonsalves, Dennis, Wang, Lei, and Alam, Maqsudul

Published
2008
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47. Low X/Y divergence in four pairs of papaya sex-linked genes

Author

Yu, Qingyi, Hou, Shaobin, Feltus, F. Alex, Jones, Meghan R., Murray, Jan E., Veatch, Olivia, Lemke, Cornelia, Saw, Jimmy H., Moore, Richard C., Thimmapuram, Jyothi, Liu, Lei, Moore, Paul H., Alam, Maqsudul, Jiang, Jiming, Paterson, Andrew H., and Ming, Ray

Published
2008

48. Ergot alkaloid exposure during gestation alters: 3. Fetal growth, muscle fiber development, and miRNA transcriptome()

Author

Greene, Maslyn A, Britt, Jessica L, Powell, Rhonda R, Feltus, F Alex, Bridges, William C, Bruce, Terri, Klotz, James L, Miller, Markus F, and Duckett, Susan K

Subjects
Festuca, Ergot Alkaloids, Sheep, Muscle Fibers, Skeletal, Brain, Toxicology, Placentation, Fetal Development, Ergotamines, MicroRNAs, Fetal Weight, Pregnancy, Seeds, Endophytes, Animals, Female, Transcriptome
Abstract

The objective of this study was to assess how exposure to ergot alkaloids during 2 stages of gestation alters fetal growth, muscle fiber formation, and miRNA expression. Pregnant ewes (n = 36; BW = 83.26 ± 8.14 kg; 4/group; 9 groups) were used in a 2 × 2 factorial arrangement with 2 tall fescue seed treatments [endophyte-infected (E+) vs. endophyte-free (E−)] fed during 2 stages of gestation (MID, days 35 to 85 vs. LATE, days 86 to 133), which created 4 possible treatments (E−/E−, E+/E−, E−/E+, or E+/E+). Ewes were individually fed a total mixed ration containing E+ or E− fescue seed according to treatment assignment. Terminal surgeries were conducted on day 133 of gestation for the collection of fetal measurements and muscle samples. Data were analyzed as a 2 × 2 factorial with fescue treatment, stage of gestation, and 2-way interaction as fixed effects. Fetuses exposed to E+ seed during LATE gestation had reduced (P = 0.0020) fetal BW by 10% compared with E− fetuses; however, fetal body weight did not differ (P = 0.41) with E+ exposure during MID gestation. Fetuses from ewes fed E+ seed during MID and LATE gestation tended to have smaller (P = 0.058) kidney weights compared with E− fetuses. Liver weight was larger (P = 0.0069) in fetuses fed E− during LATE gestation compared with E+. Fetal brain weight did not differ by fescue treatment fed during MID (P = 0.36) or LATE (P = 0.40) gestation. The percentage of brain to empty body weight (EBW) was greater (P = 0.0048) in fetuses from ewes fed E+ fescue seed during LATE gestation, which is indicative of intrauterine growth restriction (IUGR). Primary muscle fiber number was lower (P = 0.0005) in semitendinosus (STN) of fetuses exposed to E+ during MID and/or LATE gestation compared with E−/E−. miRNA sequencing showed differential expression (P < 0.010) of 6 novel miRNAs including bta-miR-652_R+1, mdo-miR-22-3p, bta-miR-1277_R-1, ppy-miR-133a_L+1_1ss5TG, hsa-miR-129-1-3p, and ssc-miR-615 in fetal STN muscle. These miRNA are associated with glucose transport, insulin signaling, intracellular ATP, hypertension, or adipogenesis. This work supports the hypothesis that E+ tall fescue seed fed during late gestation reduces fetal weight and causes asymmetrical growth, which is indicative of IUGR. Changes in primary fiber number and miRNA of STN indicate that exposure to E+ fescue fed during MID and LATE gestation alters fetal muscle development that may affect postnatal muscle growth and meat quality.

Published
2019

50. Addressing noise in co-expression network construction.

Author

Burns, Joshua J R, Shealy, Benjamin T, Greer, Mitchell S, Hadish, John A, McGowan, Matthew T, Biggs, Tyler, Smith, Melissa C, Feltus, F Alex, and Ficklin, Stephen P

Subjects
GENE regulatory networks, CONFOUNDING variables, NOISE, GENE expression, FALSE positive error
Abstract

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The 'one-size-fits-all' approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow. [ABSTRACT FROM AUTHOR]

Published
2022
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