1
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Beck KL, Haiminen N, Chambliss D, Edlund S, Kunitomi M, Huang BC, Kong N, Ganesan B, Baker R, Markwell P, Kawas B, Davis M, Prill RJ, Krishnareddy H, Seabolt E, Marlowe CH, Pierre S, Quintanar A, Parida L, Dubois G, Kaufman J, Weimer BC. Monitoring the microbiome for food safety and quality using deep shotgun sequencing. NPJ Sci Food 2021; 5:3. [PMID: 33558514 PMCID: PMC7870667 DOI: 10.1038/s41538-020-00083-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 11/24/2020] [Indexed: 01/30/2023] Open
Abstract
In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.
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Affiliation(s)
- Kristen L. Beck
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Niina Haiminen
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481554.9IBM T.J. Watson Research Center, Yorktown Heights, Ossining, NY USA
| | - David Chambliss
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Stefan Edlund
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Mark Kunitomi
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - B. Carol Huang
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
| | - Nguyet Kong
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
| | - Balasubramanian Ganesan
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China ,grid.507690.dWisdom Health, A Division of Mars Petcare, Vancouver, WA USA
| | - Robert Baker
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China
| | - Peter Markwell
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China
| | - Ban Kawas
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Matthew Davis
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Robert J. Prill
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Harsha Krishnareddy
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Ed Seabolt
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Carl H. Marlowe
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.418312.d0000 0001 2187 1663Bio-Rad Laboratories, Hercules, CA USA
| | - Sophie Pierre
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481801.40000 0004 0623 3323Bio-Rad, Food Science Division, MArnes-La-Coquette, France
| | - André Quintanar
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481801.40000 0004 0623 3323Bio-Rad, Food Science Division, MArnes-La-Coquette, France
| | - Laxmi Parida
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481554.9IBM T.J. Watson Research Center, Yorktown Heights, Ossining, NY USA
| | - Geraud Dubois
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - James Kaufman
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Bart C. Weimer
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
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2
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McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, Minot SS, Danko D, Foox J, Ahsanuddin S, Tighe S, Hasan NA, Subramanian P, Moffat K, Levy S, Lonardi S, Greenfield N, Colwell RR, Rosen GL, Mason CE. Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol 2019; 20:72. [PMID: 30953547 PMCID: PMC6450011 DOI: 10.1186/s13059-019-1687-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 04/01/2019] [Indexed: 11/10/2022] Open
Abstract
Following publication of the original article [1], the authors would like to highlight the following two corrections.
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Affiliation(s)
- Alexa B R McIntyre
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Rachid Ounit
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA.,School of Medicine, New York Medical College, Valhalla, NY, 10595, USA
| | - Robert J Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA, 95120, USA
| | - Elizabeth Hénaff
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Noah Alexander
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Samuel S Minot
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - David Danko
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Sofia Ahsanuddin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Scott Tighe
- University of Vermont, Burlington, VT, 05405, USA
| | - Nur A Hasan
- CosmosID, Inc, Rockville, MD, 20850, USA.,Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies (UMIACS), College Park, MD, 20742, USA
| | | | | | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Stefano Lonardi
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Nick Greenfield
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - Rita R Colwell
- CosmosID, Inc, Rockville, MD, 20850, USA.,Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gail L Rosen
- Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA. .,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA. .,The Feil Family Brain and Mind Research Institute, New York, NY, 10065, USA.
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3
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Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, Prill RJ, Tripathi A, Gibbons SM, Ackermann G, Navas-Molina JA, Janssen S, Kopylova E, Vázquez-Baeza Y, González A, Morton JT, Mirarab S, Zech Xu Z, Jiang L, Haroon MF, Kanbar J, Zhu Q, Jin Song S, Kosciolek T, Bokulich NA, Lefler J, Brislawn CJ, Humphrey G, Owens SM, Hampton-Marcell J, Berg-Lyons D, McKenzie V, Fierer N, Fuhrman JA, Clauset A, Stevens RL, Shade A, Pollard KS, Goodwin KD, Jansson JK, Gilbert JA, Knight R. A communal catalogue reveals Earth's multiscale microbial diversity. Nature 2017; 551:457-463. [PMID: 29088705 PMCID: PMC6192678 DOI: 10.1038/nature24621] [Citation(s) in RCA: 1219] [Impact Index Per Article: 174.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 10/10/2017] [Indexed: 02/07/2023]
Abstract
Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.
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Affiliation(s)
- Luke R Thompson
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA.,Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
| | - Jon G Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Amnon Amir
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Joshua Ladau
- The Gladstone Institutes and University of California San Francisco, San Francisco, California, USA
| | - Kenneth J Locey
- Department of Biology, Indiana University, Bloomington, Indiana, USA
| | - Robert J Prill
- Industrial and Applied Genomics, IBM Almaden Research Center, San Jose, California, USA
| | - Anupriya Tripathi
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Division of Biological Sciences, University of California San Diego, La Jolla, California, USA.,Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA
| | - Sean M Gibbons
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jose A Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Stefan Janssen
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Evguenia Kopylova
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Yoshiki Vázquez-Baeza
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - James T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Zhenjiang Zech Xu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Lingjing Jiang
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
| | - Mohamed F Haroon
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Jad Kanbar
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Se Jin Song
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Nicholas A Bokulich
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Joshua Lefler
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Colin J Brislawn
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Gregory Humphrey
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Sarah M Owens
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA
| | - Jarrad Hampton-Marcell
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Donna Berg-Lyons
- BioFrontiers Institute, University of Colorado, Boulder, Colorado, USA
| | - Valerie McKenzie
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
| | - Noah Fierer
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA.,Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
| | - Jed A Fuhrman
- Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
| | - Aaron Clauset
- BioFrontiers Institute, University of Colorado, Boulder, Colorado, USA.,Department of Computer Science, University of Colorado, Boulder, Colorado, USA
| | - Rick L Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Computer Science, University of Chicago, Chicago, Illinois, USA
| | - Ashley Shade
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA.,Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA.,Program in Ecology, Evolutionary Biology and Behavior, Michigan State University, East Lansing, Michigan, USA
| | - Katherine S Pollard
- The Gladstone Institutes and University of California San Francisco, San Francisco, California, USA
| | - Kelly D Goodwin
- Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jack A Gilbert
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Surgery, University of Chicago, Chicago, Illinois, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
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4
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McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, Minot SS, Danko D, Foox J, Ahsanuddin S, Tighe S, Hasan NA, Subramanian P, Moffat K, Levy S, Lonardi S, Greenfield N, Colwell RR, Rosen GL, Mason CE. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol 2017; 18:182. [PMID: 28934964 PMCID: PMC5609029 DOI: 10.1186/s13059-017-1299-7] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 08/16/2017] [Indexed: 12/25/2022] Open
Abstract
Background One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. Results In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. Conclusions This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1299-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexa B R McIntyre
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Rachid Ounit
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA.,School of Medicine, New York Medical College, Valhalla, NY, 10595, USA
| | - Robert J Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA, 95120, USA
| | - Elizabeth Hénaff
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Noah Alexander
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Samuel S Minot
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - David Danko
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Sofia Ahsanuddin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Scott Tighe
- University of Vermont, Burlington, VT, 05405, USA
| | - Nur A Hasan
- CosmosID, Inc, Rockville, MD, 20850, USA.,Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies (UMIACS), College Park, MD, 20742, USA
| | | | | | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Stefano Lonardi
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Nick Greenfield
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - Rita R Colwell
- CosmosID, Inc, Rockville, MD, 20850, USA.,Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gail L Rosen
- Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA. .,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA. .,The Feil Family Brain and Mind Research Institute, New York, NY, 10065, USA.
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5
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Maritz JM, Sullivan SA, Prill RJ, Aksoy E, Scheid P, Carlton JM. Filthy lucre: A metagenomic pilot study of microbes found on circulating currency in New York City. PLoS One 2017; 12:e0175527. [PMID: 28384336 PMCID: PMC5383295 DOI: 10.1371/journal.pone.0175527] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 03/27/2017] [Indexed: 12/25/2022] Open
Abstract
Background Paper currency by its very nature is frequently transferred from one person to another and represents an important medium for human contact with—and potential exchange of—microbes. In this pilot study, we swabbed circulating $1 bills obtained from a New York City bank in February (Winter) and June (Summer) 2013 and used shotgun metagenomic sequencing to profile the communities found on their surface. Using basic culture conditions, we also tested whether viable microbes could be recovered from bills. Results Shotgun metagenomics identified eukaryotes as the most abundant sequences on money, followed by bacteria, viruses and archaea. Eukaryotic assemblages were dominated by human, other metazoan and fungal taxa. The currency investigated harbored a diverse microbial population that was dominated by human skin and oral commensals, including Propionibacterium acnes, Staphylococcus epidermidis and Micrococcus luteus. Other taxa detected not associated with humans included Lactococcus lactis and Streptococcus thermophilus, microbes typically associated with dairy production and fermentation. Culturing results indicated that viable microbes can be isolated from paper currency. Conclusions We conducted the first metagenomic characterization of the surface of paper money in the United States, establishing a baseline for microbes found on $1 bills circulating in New York City. Our results suggest that money amalgamates DNA from sources inhabiting the human microbiome, food, and other environmental inputs, some of which can be recovered as viable organisms. These monetary communities may be maintained through contact with human skin, and DNA obtained from money may provide a record of human behavior and health. Understanding these microbial profiles is especially relevant to public health as money could potentially mediate interpersonal transfer of microbes.
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Affiliation(s)
- Julia M. Maritz
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States of America
| | - Steven A. Sullivan
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States of America
| | - Robert J. Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, California, United States of America
| | - Emre Aksoy
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States of America
| | - Paul Scheid
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States of America
| | - Jane M. Carlton
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States of America
- * E-mail:
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6
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Zolnik CP, Prill RJ, Falco RC, Daniels TJ, Kolokotronis SO. Microbiome changes through ontogeny of a tick pathogen vector. Mol Ecol 2016; 25:4963-77. [DOI: 10.1111/mec.13832] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 08/19/2016] [Accepted: 08/29/2016] [Indexed: 01/03/2023]
Affiliation(s)
- Christine P. Zolnik
- Department of Biological Sciences; Fordham University; 441 East Fordham Road Bronx NY 10458 USA
- Vector Ecology Laboratory; Louis Calder Center-Biological Field Station; Fordham University; 53 Whippoorwill Road Armonk NY 10504 USA
| | - Robert J. Prill
- IBM Almaden Research Center; 650 Harry Road San Jose CA 95120 USA
| | - Richard C. Falco
- New York State Department of Health; Louis Calder Center-Biological Field Station; Fordham University; 53 Whippoorwill Road Armonk NY 10504 USA
| | - Thomas J. Daniels
- Vector Ecology Laboratory; Louis Calder Center-Biological Field Station; Fordham University; 53 Whippoorwill Road Armonk NY 10504 USA
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7
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Patro R, Norel R, Prill RJ, Saez-Rodriguez J, Lorenz P, Steinbeck F, Ziems B, Luštrek M, Barbarini N, Tiengo A, Bellazzi R, Thiesen HJ, Stolovitzky G, Kingsford C. A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin. BMC Bioinformatics 2016; 17:155. [PMID: 27059896 PMCID: PMC4826543 DOI: 10.1186/s12859-016-1008-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 03/31/2016] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). RESULTS We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. CONCLUSIONS Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.
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Affiliation(s)
- Rob Patro
- />Department of Computer Science, Stony Brook, NY, USA
| | - Raquel Norel
- />IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Robert J. Prill
- />IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Julio Saez-Rodriguez
- />European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK
| | - Peter Lorenz
- />Institute of Immunology, University of Rostock, Rostock, Germany
| | - Felix Steinbeck
- />Institute of Immunology, University of Rostock, Rostock, Germany
- />Gesellschaft für Individualisierte Medizin (IndyMed) mbH, Rostock, Germany
| | - Bjoern Ziems
- />Gesellschaft für Individualisierte Medizin (IndyMed) mbH, Rostock, Germany
| | - Mitja Luštrek
- />Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Nicola Barbarini
- />Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Alessandra Tiengo
- />Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Riccardo Bellazzi
- />Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hans-Jürgen Thiesen
- />Institute of Immunology, University of Rostock, Rostock, Germany
- />Gesellschaft für Individualisierte Medizin (IndyMed) mbH, Rostock, Germany
| | | | - Carl Kingsford
- />Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
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Cokelaer T, Bansal M, Bare C, Bilal E, Bot BM, Chaibub Neto E, Eduati F, de la Fuente A, Gönen M, Hill SM, Hoff B, Karr JR, Küffner R, Menden MP, Meyer P, Norel R, Pratap A, Prill RJ, Weirauch MT, Costello JC, Stolovitzky G, Saez-Rodriguez J. DREAMTools: a Python package for scoring collaborative challenges. F1000Res 2015; 4:1030. [PMID: 27134723 PMCID: PMC4837986 DOI: 10.12688/f1000research.7118.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/01/2016] [Indexed: 01/30/2023] Open
Abstract
UNLABELLED DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. AVAILABILITY DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.
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Affiliation(s)
- Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK; Bioinformatics and Biostatistics Hub, C3BI, Institut Pasteur, Paris, France
| | - Mukesh Bansal
- Department of Systems Biology, Columbia University, New York, USA
| | | | - Erhan Bilal
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | | | | | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK
| | - Alberto de la Fuente
- Leibniz Institute for Farm Animal Biology, Institute of Genetics and Biometry, Dummerstorf, Germany
| | - Mehmet Gönen
- Oregon Health & Science University, Portland, OR, USA
| | - Steven M Hill
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | | | - Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Robert Küffner
- Institute of Bioinformatics and Systems Biology, German Research Center for Environmental Health, Munich, Germany
| | - Michael P Menden
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK
| | - Pablo Meyer
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | - Raquel Norel
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | | | | | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology and Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - James C Costello
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Gustavo Stolovitzky
- IBM, TJ Watson, Computational Biology Center, New York, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK; RWTH Aachen University Medical Hospital, Joint Research Centre for Computational Biomedicine (JRCCOMBINE), Aachen, Germany
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9
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Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, Maritz JM, Reeves D, Gandara J, Chhangawala S, Ahsanuddin S, Simmons A, Nessel T, Sundaresh B, Pereira E, Jorgensen E, Kolokotronis SO, Kirchberger N, Garcia I, Gandara D, Dhanraj S, Nawrin T, Saletore Y, Alexander N, Vijay P, Hénaff EM, Zumbo P, Walsh M, O'Mullan GD, Tighe S, Dudley JT, Dunaif A, Ennis S, O'Halloran E, Magalhaes TR, Boone B, Jones AL, Muth TR, Paolantonio KS, Alter E, Schadt EE, Garbarino J, Prill RJ, Carlton JM, Levy S, Mason CE. Modern Methods for Delineating Metagenomic Complexity. Cell Syst 2015; 1:6-7. [PMID: 27135684 DOI: 10.1016/j.cels.2015.07.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 07/16/2015] [Accepted: 07/16/2015] [Indexed: 01/31/2023]
Affiliation(s)
- Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; School of Earth and Environmental Sciences, City University of New York (CUNY) Queens College, Flushing, NY 11367, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Shanin Chowdhury
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; CUNY Hunter College, New York 10065, NY, USA
| | - Dyala Jaroudi
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Collin Boyer
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Nick Bernstein
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Julia M Maritz
- Center for Genomics, New York University, New York, NY 10065, USA
| | - Darryl Reeves
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY Center for Genomics, USA
| | - Jorge Gandara
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sagar Chhangawala
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sofia Ahsanuddin
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; Department of Biology, CUNY Brooklyn College, Brooklyn, NY 11210, USA
| | - Amber Simmons
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | | | | | | | | | | | - Nell Kirchberger
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Isaac Garcia
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - David Gandara
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sean Dhanraj
- Department of Biology, CUNY Brooklyn College, Brooklyn, NY 11210, USA
| | - Tanzina Nawrin
- Department of Biology, CUNY Brooklyn College, Brooklyn, NY 11210, USA
| | - Yogesh Saletore
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY Center for Genomics, USA
| | - Noah Alexander
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Priyanka Vijay
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY Center for Genomics, USA
| | - Elizabeth M Hénaff
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Paul Zumbo
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Michael Walsh
- State University of New York, Downstate, Brooklyn, NY 11203, USA
| | - Gregory D O'Mullan
- School of Earth and Environmental Sciences, City University of New York (CUNY) Queens College, Flushing, NY 11367, USA
| | - Scott Tighe
- University of Vermont, Burlington, VT 05405, USA
| | - Joel T Dudley
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029 USA
| | - Anya Dunaif
- Rockefeller University, New York, NY 10065, USA
| | - Sean Ennis
- National Children's Research Centre, Our Lady's Children's Hospital, Dublin 12, Ireland; Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College, Dublin 12, Ireland
| | - Eoghan O'Halloran
- National Children's Research Centre, Our Lady's Children's Hospital, Dublin 12, Ireland
| | - Tiago R Magalhaes
- Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College, Dublin 12, Ireland
| | - Braden Boone
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Angela L Jones
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Theodore R Muth
- Department of Biology, CUNY Brooklyn College, Brooklyn, NY 11210, USA
| | | | | | - Eric E Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029 USA
| | | | - Robert J Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA 95120, USA
| | - Jane M Carlton
- Center for Genomics, New York University, New York, NY 10065, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA; The Feil Family Brain and Mind Research Institute, New York, NY 10065, USA.
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10
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Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, Maritz JM, Reeves D, Gandara J, Chhangawala S, Ahsanuddin S, Simmons A, Nessel T, Sundaresh B, Pereira E, Jorgensen E, Kolokotronis SO, Kirchberger N, Garcia I, Gandara D, Dhanraj S, Nawrin T, Saletore Y, Alexander N, Vijay P, Hénaff EM, Zumbo P, Walsh M, O'Mullan GD, Tighe S, Dudley JT, Dunaif A, Ennis S, O'Halloran E, Magalhaes TR, Boone B, Jones AL, Muth TR, Paolantonio KS, Alter E, Schadt EE, Garbarino J, Prill RJ, Carlton JM, Levy S, Mason CE. Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics. Cell Syst 2015; 1:97-97.e3. [PMID: 27135689 DOI: 10.1016/j.cels.2015.07.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Abstract
Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.
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Affiliation(s)
- Robert J. Prill
- IBM T. J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, N.Y. 10598, United States of America
| | - Robert Vogel
- ImmunoDynamics Group, Program in Computational Biology and Immunology, Memorial Sloan- Kettering Cancer Center, 1275 York Avenue, Box 460, New York, N.Y. 10065, United States of America
| | - Guillermo A. Cecchi
- IBM T. J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, N.Y. 10598, United States of America
| | - Grégoire Altan-Bonnet
- ImmunoDynamics Group, Program in Computational Biology and Immunology, Memorial Sloan- Kettering Cancer Center, 1275 York Avenue, Box 460, New York, N.Y. 10065, United States of America
| | - Gustavo Stolovitzky
- IBM T. J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, N.Y. 10598, United States of America
- * E-mail:
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12
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Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, Maritz JM, Reeves D, Gandara J, Chhangawala S, Ahsanuddin S, Simmons A, Nessel T, Sundaresh B, Pereira E, Jorgensen E, Kolokotronis SO, Kirchberger N, Garcia I, Gandara D, Dhanraj S, Nawrin T, Saletore Y, Alexander N, Vijay P, Hénaff EM, Zumbo P, Walsh M, O'Mullan GD, Tighe S, Dudley JT, Dunaif A, Ennis S, O'Halloran E, Magalhaes TR, Boone B, Jones AL, Muth TR, Paolantonio KS, Alter E, Schadt EE, Garbarino J, Prill RJ, Carlton JM, Levy S, Mason CE. Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics. Cell Syst 2015; 1:72-87. [PMID: 26594662 PMCID: PMC4651444 DOI: 10.1016/j.cels.2015.01.001] [Citation(s) in RCA: 176] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The panoply of microorganisms and other species present in our environment influence human health and disease, especially in cities, but have not been profiled with metagenomics at a city-wide scale. We sequenced DNA from surfaces across the entire New York City (NYC) subway system, the Gowanus Canal, and public parks. Nearly half of the DNA (48%) does not match any known organism; identified organisms spanned 1,688 bacterial, viral, archaeal, and eukaryotic taxa, which were enriched for harmless genera associated with skin (e.g., Acinetobacter). Predicted ancestry of human DNA left on subway surfaces can recapitulate U.S. Census demographic data, and bacterial signatures can reveal a station’s history, such as marine-associated bacteria in a hurricane-flooded station. Some evidence of pathogens was found (Bacillus anthracis), but a lack of reported cases in NYC suggests that the pathogens represent a normal, urban microbiome. This baseline metagenomic map of NYC could help long-term disease surveillance, bioterrorism threat mitigation, and health management in the built environment of cities.
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Affiliation(s)
- Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; School of Earth and Environmental Sciences, City University of New York (CUNY) Queens College, Flushing, NY 11367, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Shanin Chowdhury
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; CUNY Hunter College, New York, NY 10065, USA
| | - Dyala Jaroudi
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Collin Boyer
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Nick Bernstein
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Julia M Maritz
- Center for Genomics, New York University, New York, NY 10003, USA
| | - Darryl Reeves
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY 10065, USA
| | - Jorge Gandara
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sagar Chhangawala
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sofia Ahsanuddin
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; CUNY Brooklyn College, Department of Biology, Brooklyn, NY 11210, USA
| | - Amber Simmons
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | | | | | | | | | | | - Nell Kirchberger
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Isaac Garcia
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - David Gandara
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Sean Dhanraj
- CUNY Brooklyn College, Department of Biology, Brooklyn, NY 11210, USA
| | - Tanzina Nawrin
- CUNY Brooklyn College, Department of Biology, Brooklyn, NY 11210, USA
| | - Yogesh Saletore
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY 10065, USA
| | - Noah Alexander
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Priyanka Vijay
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; Tri-Institutional Program on Computational Biology and Medicine (CBM), New York, NY 10065, USA
| | - Elizabeth M Hénaff
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Paul Zumbo
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Michael Walsh
- State University of New York, Downstate, Brooklyn, NY 11203, USA
| | - Gregory D O'Mullan
- School of Earth and Environmental Sciences, City University of New York (CUNY) Queens College, Flushing, NY 11367, USA
| | - Scott Tighe
- University of Vermont, Burlington, VT 05405, USA
| | - Joel T Dudley
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anya Dunaif
- Rockefeller University, New York, NY 10065, USA
| | - Sean Ennis
- Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College Dublin 4, Ireland ; National Centre for Medical Genetics, Our Lady's Children's Hospital, Dublin 12, Ireland
| | - Eoghan O'Halloran
- Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College Dublin 4, Ireland
| | - Tiago R Magalhaes
- Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College Dublin 4, Ireland ; National Centre for Medical Genetics, Our Lady's Children's Hospital, Dublin 12, Ireland
| | - Braden Boone
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Angela L Jones
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Theodore R Muth
- CUNY Brooklyn College, Department of Biology, Brooklyn, NY 11210, USA
| | | | | | - Eric E Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Robert J Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA 95120, USA
| | - Jane M Carlton
- Center for Genomics, New York University, New York, NY 10003, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA ; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA ; The Feil Family Brain and Mind Research Institute, New York, NY 10065, USA
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13
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Abstract
SUMMARY Computational workloads for genome-wide association studies (GWAS) are growing in scale and complexity outpacing the capabilities of single-threaded software designed for personal computers. The BlueSNP R package implements GWAS statistical tests in the R programming language and executes the calculations across computer clusters configured with Apache Hadoop, a de facto standard framework for distributed data processing using the MapReduce formalism. BlueSNP makes computationally intensive analyses, such as estimating empirical p-values via data permutation, and searching for expression quantitative trait loci over thousands of genes, feasible for large genotype-phenotype datasets. AVAILABILITY AND IMPLEMENTATION http://github.com/ibm-bioinformatics/bluesnp
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Affiliation(s)
- Hailiang Huang
- Healthcare Informatics, IBM Almaden Research Center, San Jose, CA 95120, USA
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14
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Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Stolovitzky G. Crowdsourcing network inference: the DREAM predictive signaling network challenge. Sci Signal 2011; 4:mr7. [PMID: 21900204 DOI: 10.1126/scisignal.2002212] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs-that is, outsourcing the task to the interested community-may be an effective strategy for network inference.
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Affiliation(s)
- Robert J Prill
- IBM Computational Biology Center, Yorktown Heights, NY, 10598, USA
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15
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Abstract
Regardless of how creative, innovative, and elegant our computational methods, the ultimate proof of an algorithm's worth is the experimentally validated quality of its predictions. Unfortunately, this truism is hard to reduce to practice. Usually, modelers produce hundreds to hundreds of thousands of predictions, most (if not all) of which go untested. In a best-case scenario, a small subsample of predictions (three to ten usually) is experimentally validated, as a quality control step to attest to the global soundness of the full set of predictions. However, whether this small set is even representative of the global algorithm's performance is a question usually left unaddressed. Thus, a clear understanding of the strengths and weaknesses of an algorithm most often remains elusive, especially to the experimental biologists who must decide which tool to use to address a specific problem. In this chapter, we describe the first systematic set of challenges posed to the systems biology community in the framework of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. These tests, which came to be known as the DREAM2 challenges, consist of data generously donated by participants to the DREAM project and curated in such a way as to become problems of network reconstruction and whose solutions, the actual networks behind the data, were withheld from the participants. The explanation of the resulting five challenges, a global comparison of the submissions, and a discussion of the best performing strategies are the main topics discussed.
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16
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Abstract
Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property—stability or robustness to small perturbations—is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks. The authors model how network motifs respond to small-scale perturbations and find a strong correlation between motif stability and abundance in a network, suggesting that dynamic properties of network motifs may play a role in overall network structure.
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Affiliation(s)
- Robert J Prill
- 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Pablo A Iglesias
- 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- 2Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Andre Levchenko
- 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
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17
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Suzuki M, Saxena SK, Boix E, Prill RJ, Vasandani VM, Ladner JE, Sung C, Youle RJ. Engineering receptor-mediated cytotoxicity into human ribonucleases by steric blockade of inhibitor interaction. Nat Biotechnol 1999; 17:265-70. [PMID: 10096294 DOI: 10.1038/7010] [Citation(s) in RCA: 54] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Several nonmammalian members of the RNase A superfamily exhibit anticancer activity that appears to correlate with resistance to the cytosolic ribonuclease inhibitor (RI). We mutated two human ribonucleases-pancreatic RNase (hRNAse) and eosinophil-derived neurotoxin (EDN)-to incorporate cysteine residues at putative sites of close contact to RI, but distant from the catalytic sites. Coupling of Cys89 of RNase and Cys87 of EDN to proteins at these sites via a thioether bond produced enzymatically active conjugates that were resistant to RI. To elicit cellular targeting as well as to block RI binding, transferrin was conjugated to a mutant human RNase, rhRNase(Gly89)-->Cys) and a mutant EDN (Thr87-->Cys). The transferrin-rhRNase(Gly89-->Cys) thioether conjugate was 5000-fold more toxic to U251 cells than recombinant wild-type hRNase. In addition, transferrin-targeted EDN exhibited tumor cell toxicities similar to those of hRNase. Thus, we endowed two human RI-sensitive RNases with greater cytotoxicity by increasing their resistance to RI. This strategy has the potential to generate a novel set of recombinant human proteins useful for targeted therapy of cancer.
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Affiliation(s)
- M Suzuki
- Biochemistry Section, Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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18
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Abstract
Two new radon mitigation techniques are introduced and their evaluation in a field study complemented by numerical model predictions is described. Based on numerical predictions, installation of a sub gravel membrane at the study site resulted in a factor of 2 reduction in indoor radon concentrations. Experimental data indicated that installation of "short-circuit" pipes extending between the subslab gravel and outdoors caused an additional factor of 2 decrease in the radon concentration. Consequently, the combination of these two passive radon mitigation features, called the membrane and short-circuit (MASC) technique, was associated with a factor of 4 reduction in indoor radon concentration. The energy-efficient active radon mitigation method, called efficient active subslab pressurization (EASP), required only 20% of the fan energy of conventional active subslab depressurization and reduced the indoor radon concentration by approximately a factor of 15, including the numerically-predicted impact of the sub-gravel membrane.
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Affiliation(s)
- W J Fisk
- Indoor Environment Program, Lawrence Berkeley Laboratory, Berkeley, CA, USA
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19
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Traynor GW, Apte MG, Carruthers AR, Dillworth JF, Prill RJ, Grimsrud DT, Turk BH. The effects of infiltration and insulation on the source strengths and indoor air pollution from combustion space heating appliances. JAPCA 1988; 38:1011-5. [PMID: 3221254 DOI: 10.1080/08940630.1988.10466441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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