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Davis MV, Ishiwata E, Sethi J, Sobral B. The Hows of Resident-Driven Community Empowerment towards Health Equity. Prog Community Health Partnersh 2023; 17:e3. [PMID: 38682383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
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Davis MV, Ishiwata E, Sethi J, Sobral B. The Hows of Resident-Driven Community Empowerment toward Health Equity. Prog Community Health Partnersh 2023; 17:583-593. [PMID: 38286773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
BACKGROUND This article details community engagement, design, and implementation strategies for the Raices-Xidid-Roots (RXR) Academy. RXR provided a linguistically accessible and culturally relevant curriculum to residents of Spanish and Somali-speaking immigrant, asylee, and refugee backgrounds. OBJECTIVES This study examined the implementation of the RXR program, including participation and adjustments needed to foster participant engagement and active voice, and explored participant actions to address self-identified aspirations as part of participation. RXR's goal was to empower Morgan County, Colorado, Spanish- and Somalispeaking cohorts of residents from immigrant, asylee, and refugee backgrounds such that they could autonomously plan, create, and sustain programs and organizations to meet their community needs. METHODS The observational study design included process and implementation evaluative approaches, including interview, project team meeting debriefings, and course organizer reflections, to identify and address implementation challenges, learn how the program met participants' needs, and understand keys to maintaining participant engagement. RESULTS Cultural adaptation of the content was key to maintaining consistent participant engagement, including delivering programming in participant preferred languages and tailoring curriculum to participant cultural practices. Participants indicated that language barriers had previously prevented them from accessing the content provided by the program's curriculum. Adaptations included adjusting meeting logistics, participant compensation, and unit timing. The Two RXR Academy cohorts developed initiatives that addressed community-identified needs. LESSONS LEARNED Three RXR design elements supported participant engagement and development of community power: 1) language access beyond the language justice model by providing programming in the participants' preferred language, 2) cultural adaptation of programming, and 3) community ownership and active voiceConclusions: The RXR program provided opportunities for skill development among Morgan County's non-native English-speaking residents and led to the design and implementation of resident-driven projects.
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Davis MV, Ishiwata E, Sethi J, Sobral B. The Hows of Resident-Driven Community Empowerment towards Health Equity. Prog Community Health Partnersh 2023; 17:e3. [PMID: 38286769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
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Ford KL, Leiferman J, Sobral B, Bennett JK, Moore SL, Bull S. "It depends:" a qualitative study on digital health academic-industry collaboration. Mhealth 2021; 7:57. [PMID: 34805388 PMCID: PMC8572752 DOI: 10.21037/mhealth-20-140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/07/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Academic-industry collaborations (AICs) are endorsed to alleviate challenges in digital health, but partnership experiences remain understudied. The qualitative study's objective investigated collaboration experiences between academic institutions and digital health companies. METHODS A phenomenology methodology captured experiences of AICs, eliciting perspectives from academic researchers and industry affiliates (e.g., leadership, company investigators). Semi-structured interviews probed eligible collaborators about their experiences in digital health. Analysts coded and organized data into significant statements reaching thematic saturation. RESULTS Participants (N=20) were interviewed from 6 academic institutions and 14 unique industry partners. Seven themes emerged: (I) Collaboration evolves with time, relationships, funding, and evidence; (II) Collaboration demands strong relationships and interpersonal dynamics; (III) Operational processes vary across collaborations; (IV) Collaboration climate and context matters; (V) Shared expectations lead to a better understanding of success; (VI) Overcoming challenges with recommendations; (VII) Collaboration may help navigate the global pandemic. CONCLUSIONS Digital health academic industry collaboration demands strong relationships, requiring flexible mechanisms of collaboration and cultural fit. Diverse models of collaboration exist and remain dependent on contextual factors. While no collaboration conquers all challenges in digital health, AICs may serve as a facilitator for improved digital health products, thus advancing science, promoting public health, and benefiting the economy.
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Affiliation(s)
- Kelsey L. Ford
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Jenn Leiferman
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Bruno Sobral
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
- Colorado State University, Fort Collins, CO, USA
| | - John K. Bennett
- University of Colorado Denver, Denver, CO, USA
- University of Colorado Boulder, Boulder, CO, USA
| | - Susan L. Moore
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Sheana Bull
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
- University of Colorado Denver, Denver, CO, USA
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Warren AS, Aurrecoechea C, Brunk B, Desai P, Emrich S, Giraldo-Calderón GI, Harb O, Hix D, Lawson D, Machi D, Mao C, McClelland M, Nordberg E, Shukla M, Vosshall LB, Wattam AR, Will R, Yoo HS, Sobral B. RNA-Rocket: an RNA-Seq analysis resource for infectious disease research. Bioinformatics 2015; 31:1496-8. [PMID: 25573919 PMCID: PMC4410666 DOI: 10.1093/bioinformatics/btv002] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Revised: 12/10/2014] [Accepted: 12/31/2014] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION RNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression and transcriptional structure. The methods, tools and technologies used to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material and computational infrastructure can minimize the overhead required of life science researchers. RESULTS RNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides and a user interface designed to enable both novice and experienced users of RNA-Seq data. AVAILABILITY AND IMPLEMENTATION RNA-Rocket is available at rnaseq.pathogenportal.org. Source code for this project can be found at github.com/cidvbi/PathogenPortal. CONTACT anwarren@vt.edu SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online.
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Affiliation(s)
- Andrew S Warren
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Cristina Aurrecoechea
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Brian Brunk
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Prerak Desai
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Scott Emrich
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Gloria I Giraldo-Calderón
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Omar Harb
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Deborah Hix
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Daniel Lawson
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Dustin Machi
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Chunhong Mao
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Michael McClelland
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Eric Nordberg
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Maulik Shukla
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Leslie B Vosshall
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Alice R Wattam
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Rebecca Will
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Hyun Seung Yoo
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Bruno Sobral
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA, Center for Tropical & Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA, Penn Center for Bioinformatics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46656-0369, USA, University of California, Department of Microbiology and Molecular Genetics, Irvine, California, USA and The Rockefeller University, Howard Hughes Medical Institute, New York, NY 10065, USA
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Dugan VG, Emrich SJ, Giraldo-Calderón GI, Harb OS, Newman RM, Pickett BE, Schriml LM, Stockwell TB, Stoeckert CJ, Sullivan DE, Singh I, Ward DV, Yao A, Zheng J, Barrett T, Birren B, Brinkac L, Bruno VM, Caler E, Chapman S, Collins FH, Cuomo CA, Di Francesco V, Durkin S, Eppinger M, Feldgarden M, Fraser C, Fricke WF, Giovanni M, Henn MR, Hine E, Hotopp JD, Karsch-Mizrachi I, Kissinger JC, Lee EM, Mathur P, Mongodin EF, Murphy CI, Myers G, Neafsey DE, Nelson KE, Nierman WC, Puzak J, Rasko D, Roos DS, Sadzewicz L, Silva JC, Sobral B, Squires RB, Stevens RL, Tallon L, Tettelin H, Wentworth D, White O, Will R, Wortman J, Zhang Y, Scheuermann RH. Standardized metadata for human pathogen/vector genomic sequences. PLoS One 2014; 9:e99979. [PMID: 24936976 PMCID: PMC4061050 DOI: 10.1371/journal.pone.0099979] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 05/15/2014] [Indexed: 11/18/2022] Open
Abstract
High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), informed by interactions with numerous collaborating scientists. It includes mapping to terms from other data standards initiatives, including the Genomic Standards Consortium's minimal information (MIxS) and NCBI's BioSample/BioProjects checklists and the Ontology for Biomedical Investigations (OBI). The standard includes data fields about characteristics of the organism or environmental source of the specimen, spatial-temporal information about the specimen isolation event, phenotypic characteristics of the pathogen/vector isolated, and project leadership and support. By modeling metadata fields into an ontology-based semantic framework and reusing existing ontologies and minimum information checklists, the application standard can be extended to support additional project-specific data fields and integrated with other data represented with comparable standards. The use of this metadata standard by all ongoing and future GSCID sequencing projects will provide a consistent representation of these data in the BRC resources and other repositories that leverage these data, allowing investigators to identify relevant genomic sequences and perform comparative genomics analyses that are both statistically meaningful and biologically relevant.
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Affiliation(s)
- Vivien G. Dugan
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Scott J. Emrich
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | | | - Omar S. Harb
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Ruchi M. Newman
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Brett E. Pickett
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Lynn M. Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Timothy B. Stockwell
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | | | - Dan E. Sullivan
- Cyberinfrastructure Division, Virginia Bioinformatics Institute, Blacksburg, Virginia, United States of America
| | - Indresh Singh
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Doyle V. Ward
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Alison Yao
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Bruce Birren
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Lauren Brinkac
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Vincent M. Bruno
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Elizabet Caler
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Sinéad Chapman
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Frank H. Collins
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | | | - Valentina Di Francesco
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Scott Durkin
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Mark Eppinger
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | | | - Claire Fraser
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - W. Florian Fricke
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Maria Giovanni
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Matthew R. Henn
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Erin Hine
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Julie Dunning Hotopp
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Ilene Karsch-Mizrachi
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, United States of America
| | | | - Eun Mi Lee
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Punam Mathur
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Emmanuel F. Mongodin
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Cheryl I. Murphy
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Garry Myers
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | | | - Karen E. Nelson
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - William C. Nierman
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Julia Puzak
- Kelly Government Solutions, Rockville, Maryland, United States of America
| | - David Rasko
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David S. Roos
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Lisa Sadzewicz
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Joana C. Silva
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Bruno Sobral
- Cyberinfrastructure Division, Virginia Bioinformatics Institute, Blacksburg, Virginia, United States of America
| | - R. Burke Squires
- National Institute of Allergy and Infectious Diseases, Rockville, Maryland, United States of America
| | - Rick L. Stevens
- Argonne National Laboratory, Lemont, Illinois, United States of America
| | - Luke Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Herve Tettelin
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David Wentworth
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Owen White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Rebecca Will
- Cyberinfrastructure Division, Virginia Bioinformatics Institute, Blacksburg, Virginia, United States of America
| | - Jennifer Wortman
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yun Zhang
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
| | - Richard H. Scheuermann
- J. Craig Venter Institute, Rockville, Maryland, and La Jolla, California, United States of America
- Department of Pathology, University of California San Diego, San Diego, California, United States of America
- * E-mail:
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7
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Kaput J, van Ommen B, Kremer B, Priami C, Monteiro JP, Morine M, Pepping F, Diaz Z, Fenech M, He Y, Albers R, Drevon CA, Evelo CT, Hancock REW, Ijsselmuiden C, Lumey LH, Minihane AM, Muller M, Murgia C, Radonjic M, Sobral B, West KP. Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life. Genes Nutr 2014; 9:378. [PMID: 24363221 PMCID: PMC3896628 DOI: 10.1007/s12263-013-0378-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 12/02/2013] [Indexed: 12/20/2022]
Abstract
Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum's one gene-one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems' responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.
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Affiliation(s)
- Jim Kaput
- Clinical Translation Unit, Nestle Institute of Health Sciences, Lausanne, Switzerland,
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8
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Scaria J, Mao C, Chen JW, McDonough SP, Sobral B, Chang YF. Differential stress transcriptome landscape of historic and recently emerged hypervirulent strains of Clostridium difficile strains determined using RNA-seq. PLoS One 2013; 8:e78489. [PMID: 24244315 PMCID: PMC3820578 DOI: 10.1371/journal.pone.0078489] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 09/12/2013] [Indexed: 12/18/2022] Open
Abstract
C. difficile is the most common cause of nosocomial diarrhea in North America and Europe. Genomes of individual strains of C. difficile are highly divergent. To determine how divergent strains respond to environmental changes, the transcriptomes of two historic and two recently isolated hypervirulent strains were analyzed following nutrient shift and osmotic shock. Illumina based RNA-seq was used to sequence these transcriptomes. Our results reveal that although C. difficile strains contain a large number of shared and strain specific genes, the majority of the differentially expressed genes were core genes. We also detected a number of transcriptionally active regions that were not part of the primary genome annotation. Some of these are likely to be small regulatory RNAs.
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Affiliation(s)
- Joy Scaria
- Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, New York, United States of America
| | - Chunhong Mao
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Jenn-Wei Chen
- Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, New York, United States of America
| | - Sean P. McDonough
- Department of Biomedical Sciences, Cornell University, Ithaca, New York, United States of America;
| | - Bruno Sobral
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Yung-Fu Chang
- Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, New York, United States of America
- * E-mail:
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9
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Chen JW, Scaria J, Mao C, Sobral B, Zhang S, Lawley T, Chang YF. Proteomic comparison of historic and recently emerged hypervirulent Clostridium difficile strains. J Proteome Res 2013; 12:1151-61. [PMID: 23298230 DOI: 10.1021/pr3007528] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Clostridium difficile in recent years has undergone rapid evolution and has emerged as a serious human pathogen. Proteomic approaches can improve the understanding of the diversity of this important pathogen, especially in comparing the adaptive ability of different C. difficile strains. In this study, TMT labeling and nanoLC-MS/MS driven proteomics were used to investigate the responses of four C. difficile strains to nutrient shift and osmotic shock. We detected 126 and 67 differentially expressed proteins in at least one strain under nutrition shift and osmotic shock, respectively. During nutrient shift, several components of the phosphotransferase system (PTS) were found to be differentially expressed, which indicated that the carbon catabolite repression (CCR) was relieved to allow the expression of enzymes and transporters responsible for the utilization of alternate carbon sources. Some classical osmotic shock associated proteins, such as GroEL, RecA, CspG, and CspF, and other stress proteins such as PurG and SerA were detected during osmotic shock. Furthermore, the recently emerged strains were found to contain a more robust gene network in response to both stress conditions. This work represents the first comparative proteomic analysis of historic and recently emerged hypervirulent C. difficile strains, complementing the previously published proteomics studies utilizing only one reference strain.
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Affiliation(s)
- Jenn-Wei Chen
- Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, New York 14853, United States
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10
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Lew JM, Mao C, Shukla M, Warren A, Will R, Kuznetsov D, Xenarios I, Robertson BD, Gordon SV, Schnappinger D, Cole ST, Sobral B. Database resources for the tuberculosis community. Tuberculosis (Edinb) 2013; 93:12-7. [PMID: 23332401 DOI: 10.1016/j.tube.2012.11.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Accepted: 11/27/2012] [Indexed: 12/29/2022]
Abstract
Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis.
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Affiliation(s)
- Jocelyne M Lew
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
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11
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Brennan PJ, Brosch R, Birren B, Sobral B. TBCAP; tuberculosis annotation project. Tuberculosis (Edinb) 2013; 93:1-5. [PMID: 23332402 DOI: 10.1016/j.tube.2012.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Accepted: 11/30/2012] [Indexed: 10/27/2022]
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12
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Abstract
We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties.
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Affiliation(s)
- Sampo Pyysalo
- School of Computer Science, University of Manchester, Manchester, UK
- National Centre for Text Mining, University of Manchester, Manchester, UK
| | - Tomoko Ohta
- Department of Computer Science, University of Tokyo, Tokyo, Japan
| | - Rafal Rak
- School of Computer Science, University of Manchester, Manchester, UK
- National Centre for Text Mining, University of Manchester, Manchester, UK
| | - Dan Sullivan
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Chunhong Mao
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Chunxia Wang
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Bruno Sobral
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | | | - Sophia Ananiadou
- School of Computer Science, University of Manchester, Manchester, UK
- National Centre for Text Mining, University of Manchester, Manchester, UK
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13
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Verhoef L, Williams KP, Kroneman A, Sobral B, van Pelt W, Koopmans M. Selection of a phylogenetically informative region of the norovirus genome for outbreak linkage. Virus Genes 2012; 44:8-18. [PMID: 21960432 PMCID: PMC3293504 DOI: 10.1007/s11262-011-0673-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2011] [Accepted: 09/12/2011] [Indexed: 11/18/2022]
Abstract
The recognition of a common source norovirus outbreak is supported by finding identical norovirus sequences in patients. Norovirus sequencing has been established in many (national) public health laboratories and academic centers, but often partial and different genome sequences are used. Therefore, agreement on a target sequence of sufficient diversity to resolve links between outbreaks is crucial. Although harmonization of laboratory methods is one of the keystone activities of networks that have the aim to identify common source norovirus outbreaks, this has proven difficult to accomplish, particularly in the international context. Here, we aimed at providing a method enabling identification of the genomic region informative of a common source norovirus outbreak by bio-informatic tools. The data set of 502 unique full length capsid gene sequences available from the public domain, combined with epidemiological data including linkage information was used to build over 3,000 maximum likelihood (ML) trees for different sequence lengths and regions. All ML trees were evaluated for robustness and specificity of clustering of known linked norovirus outbreaks against the background diversity of strains. Great differences were seen in the robustness of commonly used PCR targets for cluster detection. The capsid gene region spanning nucleotides 900-1,400 was identified as the region optimally substituting for the full length capsid region. Reliability of this approach depends on the quality of the background data set, and we recommend periodic reassessment of this growing data set. The approach may be applicable to multiple sequence-based data sets of other pathogens.
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Affiliation(s)
- Linda Verhoef
- National Institute for Public Health and the Environment (RIVM), Postbak 22, 3720 BA, Bilthoven, The Netherlands.
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14
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Ananiadou S, Sullivan D, Black W, Levow GA, Gillespie JJ, Mao C, Pyysalo S, Kolluru B, Tsujii J, Sobral B. Named entity recognition for bacterial Type IV secretion systems. PLoS One 2011; 6:e14780. [PMID: 21468321 PMCID: PMC3066171 DOI: 10.1371/journal.pone.0014780] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2010] [Accepted: 02/16/2011] [Indexed: 11/18/2022] Open
Abstract
Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents.
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Affiliation(s)
- Sophia Ananiadou
- School of Computer Science, University of Manchester, Manchester, United Kingdom
- National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom
| | - Dan Sullivan
- Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, Virginia, United States of America
| | - William Black
- National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom
| | - Gina-Anne Levow
- School of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Joseph J. Gillespie
- Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, Virginia, United States of America
| | - Chunhong Mao
- Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, Virginia, United States of America
| | - Sampo Pyysalo
- Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan
| | - BalaKrishna Kolluru
- School of Computer Science, University of Manchester, Manchester, United Kingdom
- National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom
| | - Junichi Tsujii
- School of Computer Science, University of Manchester, Manchester, United Kingdom
- National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom
- Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan
| | - Bruno Sobral
- Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, Virginia, United States of America
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15
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Sullivan DE, Gabbard JL, Shukla M, Sobral B. Data integration for dynamic and sustainable systems biology resources: challenges and lessons learned. Chem Biodivers 2010; 7:1124-41. [PMID: 20491070 DOI: 10.1002/cbdv.200900317] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Systems-biology and infectious-disease (host-pathogen-environment) research and development is becoming increasingly dependent on integrating data from diverse and dynamic sources. Maintaining integrated resources over long periods of time presents distinct challenges. This review describes experiences and lessons learned from integrating data in two five-year projects focused on pathosystems biology: the Pathosystems Resource Integration Center (PATRIC, http://patric.vbi.vt.edu/), with a goal of developing bioinformatics resources for the research and countermeasures-development communities based on genomics data, and the Resource Center for Biodefense Proteomics Research (RCBPR, http://www.proteomicsresource.org/), with a goal of developing resources based on the experiment data such as microarray and proteomics data from diverse sources and technologies. Some challenges include integrating genomic sequence and experiment data, data synchronization, data quality control, and usability engineering. We present examples of a variety of data-integration problems drawn from our experiences with PATRIC and RBPRC, as well as open research questions related to long-term sustainability, and describe the next steps to meeting these challenges. Novel contributions of this work include 1) an approach for addressing discrepancies between experiment results and interpreted results, and 2) expanding the range of data-integration techniques to include usability engineering at the presentation level.
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Affiliation(s)
- Daniel E Sullivan
- CyberInfrastructure Section, Virginia Bioinformatics Institute, Washington Street, MC 0477, Virginia Tech, Blacksburg, Virginia 24061, USA.
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16
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Djavani M, Crasta OR, Zhang Y, Zapata JC, Sobral B, Lechner MG, Bryant J, Davis H, Salvato MS. Gene expression in primate liver during viral hemorrhagic fever. Virol J 2009; 6:20. [PMID: 19216742 PMCID: PMC2657139 DOI: 10.1186/1743-422x-6-20] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [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] [Received: 01/12/2009] [Accepted: 02/12/2009] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Rhesus macaques infected with lymphocytic choriomeningitis virus (LCMV) provide a model for human Lassa fever. Disease begins with flu-like symptoms and progresses rapidly with fatal consequences. Previously, we profiled the blood transcriptome of LCMV-infected monkeys (M. Djavani et al J. Virol. 2007) showing distinct pre-viremic and viremic stages that discriminated virulent from benign infections. In the present study, changes in liver gene expression from macaques infected with virulent LCMV-WE were compared to gene expression in uninfected monkeys as well as to monkeys that were infected but not diseased. RESULTS Based on a functional pathway analysis of differentially expressed genes, virulent LCMV-WE had a broader effect on liver cell function than did infection with non-virulent LCMV-Armstrong. During the first few days after infection, LCMV altered expression of genes associated with energy production, including fatty acid and glucose metabolism. The transcriptome profile resembled that of an organism in starvation: mRNA for acetyl-CoA carboxylase, a key enzyme of fatty acid synthesis was reduced while genes for enzymes in gluconeogenesis were up-regulated. Expression was also altered for genes associated with complement and coagulation cascades, and with signaling pathways involving STAT1 and TGF-beta. CONCLUSION Most of the 4500 differentially expressed transcripts represented a general response to both virulent and mild infections. However, approximately 250 of these transcripts had significantly different expression in virulent infections as compared to mild infections, with approximately 30 of these being differentially regulated during the pre-viremic stage of infection. The genes that are expressed early and differently in mild and virulent disease are potential biomarkers for prognosis and triage of acute viral disease.
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Affiliation(s)
- Mahmoud Djavani
- Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
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17
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Zhang C, Crasta O, Cammer S, Will R, Kenyon R, Sullivan D, Yu Q, Sun W, Jha R, Liu D, Xue T, Zhang Y, Moore M, McGarvey P, Huang H, Chen Y, Zhang J, Mazumder R, Wu C, Sobral B. An emerging cyberinfrastructure for biodefense pathogen and pathogen-host data. Nucleic Acids Res 2008; 36:D884-91. [PMID: 17984082 PMCID: PMC2239001 DOI: 10.1093/nar/gkm903] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2007] [Revised: 10/04/2007] [Accepted: 10/05/2007] [Indexed: 01/07/2023] Open
Abstract
The NIAID-funded Biodefense Proteomics Resource Center (RC) provides storage, dissemination, visualization and analysis capabilities for the experimental data deposited by seven Proteomics Research Centers (PRCs). The data and its publication is to support researchers working to discover candidates for the next generation of vaccines, therapeutics and diagnostics against NIAID's Category A, B and C priority pathogens. The data includes transcriptional profiles, protein profiles, protein structural data and host-pathogen protein interactions, in the context of the pathogen life cycle in vivo and in vitro. The database has stored and supported host or pathogen data derived from Bacillus, Brucella, Cryptosporidium, Salmonella, SARS, Toxoplasma, Vibrio and Yersinia, human tissue libraries, and mouse macrophages. These publicly available data cover diverse data types such as mass spectrometry, yeast two-hybrid (Y2H), gene expression profiles, X-ray and NMR determined protein structures and protein expression clones. The growing database covers over 23 000 unique genes/proteins from different experiments and organisms. All of the genes/proteins are annotated and integrated across experiments using UniProt Knowledgebase (UniProtKB) accession numbers. The web-interface for the database enables searching, querying and downloading at the level of experiment, group and individual gene(s)/protein(s) via UniProtKB accession numbers or protein function keywords. The system is accessible at http://www.proteomicsresource.org/.
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Affiliation(s)
- C. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - O. Crasta
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - S. Cammer
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Will
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Kenyon
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Sullivan
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Q. Yu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - W. Sun
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Jha
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Liu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - T. Xue
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - M. Moore
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - P. McGarvey
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - H. Huang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Chen
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - J. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Mazumder
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - C. Wu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - B. Sobral
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
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18
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Li S, Brazhnik P, Sobral B, Tyson JJ. A quantitative study of the division cycle of Caulobacter crescentus stalked cells. PLoS Comput Biol 2007; 4:e9. [PMID: 18225942 PMCID: PMC2217572 DOI: 10.1371/journal.pcbi.0040009] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.8] [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: 03/01/2007] [Accepted: 12/05/2007] [Indexed: 11/18/2022] Open
Abstract
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells. From published experimental evidence, we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on the synthesis and degradation of three “master regulator” proteins (CtrA, GcrA, and DnaA), is converted into a quantitative model, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts for important details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. It reproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, and predicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating the cell cycle of C. crescentus are conserved among many genera of α-proteobacteria, the proposed mechanism may be applicable to other species of importance in agriculture and medicine. The cell cycle is the sequence of events by which a growing cell replicates all its components and divides them more or less evenly between two daughter cells. The timing and spatial organization of these events are controlled by gene–protein interaction networks of great complexity. A challenge for computational biology is to build realistic, accurate, predictive mathematical models of these control systems in a variety of organisms, both eukaryotes and prokaryotes. To this end, we present a model of a portion of the molecular network controlling DNA synthesis, cell cycle–related gene expression, DNA methylation, and cell division in stalked cells of the α-proteobacterium Caulobacter crescentus. The model is formulated in terms of nonlinear ordinary differential equations for the major cell cycle regulatory proteins in Caulobacter: CtrA, GcrA, DnaA, CcrM, and DivK. Kinetic rate constants are estimated, and the model is tested against available experimental observations on wild-type and mutant cells. The model is viewed as a starting point for more comprehensive models of the future that will account, in addition, for the spatial asymmetry of Caulobacter reproduction (swarmer cells as well as stalked cells), the correlation of cell growth and division, and cell cycle checkpoints.
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Affiliation(s)
- Shenghua Li
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Paul Brazhnik
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Bruno Sobral
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - John J Tyson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * To whom correspondence should be addressed. E-mail:
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19
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Koterski J, Twenhafel N, Porter A, Reed DS, Martino-Catt S, Sobral B, Crasta O, Downey T, DaSilva L. Gene expression profiling of nonhuman primates exposed to aerosolized Venezuelan equine encephalitis virus. ACTA ACUST UNITED AC 2007; 51:462-72. [PMID: 17894805 DOI: 10.1111/j.1574-695x.2007.00319.x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [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: 01/12/2023]
Abstract
Host responses to Venezuelan equine encephalitis viruses (VEEV) were studied in cynomolgus macaques after aerosol exposure to the epizootic virus. Changes in global gene expression were assessed for the brain, lungs, and spleen. In the brain, major histocompatibility complex (MHC) class I transcripts were induced, while the expression of S100b, a factor associated with brain injury, was inhibited, as was expression of the encephalitogenic gene MOG. Cytokine-mediated signals were affected by infection, including those involving IFN-mediated antiviral activity (IRF-7, OAS, and Mx transcripts), and the increased transcription of caspases. Induction of a few immunologically relevant genes (e.g. IFITM1 and STAT1) was common to all tested tissues. Herein, both tissue-specific and nontissue specific transcriptional changes in response to VEEV are described, including induction of IFN-regulated transcripts and cytokine-induced apoptotic factors, in addition to cellular factors in the brain that may be descriptive of the health status of the brain during the infectious process. Altogether, this work provides novel information on common and tissue-specific host responses against VEEV in a nonhuman primate model of aerosol exposure.
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Affiliation(s)
- James Koterski
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702-9211, USA
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20
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Yu G, Snyder E, Boyle S, Crasta O, Czar M, Mane S, Purkayastha A, Sobral B, Setubal J. A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case. Nucleic Acids Res 2007; 35:3953-62. [PMID: 17553834 PMCID: PMC1919506 DOI: 10.1093/nar/gkm377] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity.
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Affiliation(s)
- G.X. Yu
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - E.E. Snyder
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - S.M. Boyle
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - O.R. Crasta
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - M. Czar
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - S.P. Mane
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - A. Purkayastha
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - B. Sobral
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
| | - J.C. Setubal
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061, Department of Biology and Department of Computer Science, Boise State University, Boise, ID 83726 and Center for Molecular Medicine and Infectious Diseases, Virginia–Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA
- *To whom correspondence should be addressed. +1 540 231 9464+1 540 231 2606
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21
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Djavani MM, Crasta OR, Zapata JC, Fei Z, Folkerts O, Sobral B, Swindells M, Bryant J, Davis H, Pauza CD, Lukashevich IS, Hammamieh R, Jett M, Salvato MS. Early blood profiles of virus infection in a monkey model for Lassa fever. J Virol 2007; 81:7960-73. [PMID: 17522210 PMCID: PMC1951294 DOI: 10.1128/jvi.00536-07] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Acute arenavirus disease in primates, like Lassa hemorrhagic fever in humans, begins with flu-like symptoms and leads to death approximately 2 weeks after infection. Our goal was to identify molecular changes in blood that are related to disease progression. Rhesus macaques (Macaca mulatta) infected intravenously with a lethal dose of lymphocytic choriomeningitis virus (LCMV) provide a model for Lassa virus infection of humans. Blood samples taken before and during the course of infection were used to monitor gene expression changes that paralleled disease onset. Changes in blood showed major disruptions in eicosanoid, immune response, and hormone response pathways. Approximately 12% of host genes alter their expression after LCMV infection, and a subset of these genes can discriminate between virulent and non-virulent LCMV infection. Major transcription changes have been given preliminary confirmation by quantitative PCR and protein studies and will be valuable candidates for future validation as biomarkers for arenavirus disease.
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Affiliation(s)
- Mahmoud M Djavani
- Institute of Human Virology, University of Maryland Biotechnology Institute, 725 West Lombard St., Baltimore, MD 21201, USA
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22
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Greene JM, Collins F, Lefkowitz EJ, Roos D, Scheuermann RH, Sobral B, Stevens R, White O, Di Francesco V. National Institute of Allergy and Infectious Diseases bioinformatics resource centers: new assets for pathogen informatics. Infect Immun 2007; 75:3212-9. [PMID: 17420237 PMCID: PMC1932942 DOI: 10.1128/iai.00105-07] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- John M Greene
- National Institute of Allergy and Infectious Diseases/NIH, 6610 Rockledge Drive, MSC 6603, Bethesda, MD 20850-6603, USA
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23
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Djavani M, Crasta O, Zapata JC, Fei Z, Folkerts O, Sobral B, Bryant J, Pauza C, Lukashevich I, Salvato MS. Transcription profiling of early responses to hemorrhagic fever in rhesus macaque. Retrovirology 2006. [PMCID: PMC1716834 DOI: 10.1186/1742-4690-3-s1-p16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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24
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Olden K, Call N, Sobral B, Oakes R. Toxicogenomics through the eyes of informatics: conference overview and recommendations. Environ Health Perspect 2004; 112:805-807. [PMID: 15159210 PMCID: PMC1241996 DOI: 10.1289/ehp.112-1241996] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Virginia Bioinformatics Institute, in conjunction with National Institutes of Environmental Health Sciences, hosted a conference, "Toxicogenomics through the Eyes of Informatics," in Bethesda, Maryland, USA, on 12-13 May 2003. Researchers around the world met to discuss how the application of bioinformatics tools, methodologies, and technologies will enhance our understanding of how cells and organisms respond to toxins. Conference topics included statistical methods, quantitative molecular data sets, computational algorithms for data analysis, computational modeling and simulation, challenges and opportunities in computational biology, and information technology infrastructure for data and tool management. This meeting report is a summary of conference presentations, survey results, current toxicogenomics concerns, and future directions of the toxicogenomics community. In conclusion this report discusses toxicogenomics as related to environmental agents, cell-chemical reactions, and gene-environment interactions.
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Affiliation(s)
- Kenneth Olden
- National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
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25
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Siepel A, Farmer A, Tolopko A, Zhuang M, Mendes P, Beavis W, Sobral B. ISYS: a decentralized, component-based approach to the integration of heterogeneous bioinformatics resources. Bioinformatics 2001; 17:83-94. [PMID: 11222265 DOI: 10.1093/bioinformatics/17.1.83] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.0] [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: 11/13/2022] Open
Abstract
MOTIVATION Heterogeneity of databases and software resources continues to hamper the integration of biological information. Top-down solutions are not feasible for the full-scale problem of integration across biological species and data types. Bottom-up solutions so far have not integrated, in a maximally flexible way, dynamic and interactive graphical-user-interface components with data repositories and analysis tools. RESULTS We present a component-based approach that relies on a generalized platform for component integration. The platform enables independently-developed components to synchronize their behavior and exchange services, without direct knowledge of one another. An interface-based data model allows the exchange of information with minimal component interdependency. From these interactions an integrated system results, which we call ISYSf1.gif" BORDER="0">. By allowing services to be discovered dynamically based on selected objects, ISYS encourages a kind of exploratory navigation that we believe to be well-suited for applications in genomic research.
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Affiliation(s)
- A Siepel
- National Center for Genome Resources, 2935 Rodeo Park Drive East, Santa Fe, NM 87505, USA.
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26
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Waugh M, Hraber P, Weller J, Wu Y, Chen G, Inman J, Kiphart D, Sobral B. The phytophthora genome initiative database: informatics and analysis for distributed pathogenomic research. Nucleic Acids Res 2000; 28:87-90. [PMID: 10592189 PMCID: PMC102488 DOI: 10.1093/nar/28.1.87] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/1999] [Revised: 10/18/1999] [Accepted: 10/18/1999] [Indexed: 11/14/2022] Open
Abstract
The Phytophthora Genome Initiative (PGI) is a distributed collaboration to study the genome and evolution of a particularly destructive group of plant pathogenic oomycete, with the goal of understanding the mechanisms of infection and resistance. NCGR provides informatics support for the collaboration as well as a centralized data repository. In the pilot phase of the project, several investigators prepared Phytophthora infestans and Phytophthora sojae EST and Phytophthora sojae BAC libraries and sent them to another laboratory for sequencing. Data from sequencing reactions were transferred to NCGR for analysis and curation. An analysis pipeline transforms raw data by performing simple analyses (i.e., vector removal and similarity searching) that are stored and can be retrieved by investigators using a web browser. Here we describe the database and access tools, provide an overview of the data therein and outline future plans. This resource has provided a unique opportunity for the distributed, collaborative study of a genus from which relatively little sequence data are available. Results may lead to insight into how better to control these pathogens. The homepage of PGI can be accessed at http:www.ncgr.org/pgi, with database access through the database access hyperlink.
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Affiliation(s)
- M Waugh
- The National Center for Genome Resources, 1800A Old Pecos Trail, Santa Fe, NM 87505, USA
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27
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Kamoun S, Hraber P, Sobral B, Nuss D, Govers F. Initial assessment of gene diversity for the oomycete pathogen Phytophthora infestans based on expressed sequences. Fungal Genet Biol 1999; 28:94-106. [PMID: 10587472 DOI: 10.1006/fgbi.1999.1166] [Citation(s) in RCA: 131] [Impact Index Per Article: 5.2] [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: 11/22/2022]
Abstract
A total of 1000 expressed sequence tags (ESTs) corresponding to 760 unique sequence sets were identified using random sequencing of clones from a cDNA library constructed from mycelial RNA of Phytophthora infestans. A number of software programs, represented by a relational database and an analysis pipeline, were developed for the automated analysis and storage of the EST sequence data. A set of 419 nonredundant sequences, which correspond to a total of 632 ESTs (63.2%), were identified as showing significant matches to sequences deposited in public databases. A putative cellular identity and role was assigned to all 419 sequences. All major functional categories were represented by at least several ESTs. Four novel cDNAs containing sequences related to elicitins, a family of structurally related proteins that induce the hypersensitive response and condition avirulence of P. infestans on Nicotiana plants, were among the most notable genes identified. Two of these elicitin-like cDNAs were among the most abundant cDNAs examined. The set also contained several ESTs with high sequence similarity to unique plant genes.
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Affiliation(s)
- S Kamoun
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research and Development Center, 1680 Madison Avenue, Wooster, Ohio 44691, USA
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