| NSF Org: |
DBI Division of Biological Infrastructure |
| Recipient: |
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| Initial Amendment Date: | April 9, 2012 |
| Latest Amendment Date: | June 29, 2016 |
| Award Number: | 1146300 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Jen Weller
DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
| Start Date: | September 1, 2012 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $150,701.00 |
| Total Awarded Amount to Date: | $180,805.00 |
| Funds Obligated to Date: |
FY 2013 = $65,843.00 FY 2014 = $52,023.00 FY 2016 = $30,104.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3261 WEST STATE RD SAINT BONAVENTURE NY US 14778-9800 (716)375-2435 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 14778-2500 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | ADVANCES IN BIO INFORMATICS |
| Primary Program Source: |
01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
Collaborative grants have been awarded to the University of Maryland, the University of Iowa and St. Bonaventure University to develop a methodology that exploits the wealth of annotation knowledge, notably Gene Ontology (GO) and Plant Ontology (PO) annotations of Arabidopsis genes. Motivated by the availability of rich and as yet insufficiently tapped collections of gene annotations, the project aims to facilitate the discovery of hidden knowledge that could be the basis of further scientific research. The methodology will extract patterns of interest from annotation graphs (pattern discovery). Literature-based methods will extract sentences that validate the biological meaning underlying these patterns (pattern validation). To demonstrate the methodology, the PattArAn tool (Patterns in Arabidopsis Annotations) will be customized for Arabidopsis. PattArAn will provide the user with a graphical presentation of patterns of Arabidopsis genes and associated GO and PO CV terms. Graph data mining techniques and efficient algorithmic solutions to identify dense subgraphs (DSG) and to perform graph summarization (GS) will be developed. Algorithms to mine the literature for relevant sentences for an extracted pattern (referred to as the imprint) will be developed. PattArAn will enable iterative exploration and will incorporate allied steps such as consulting gene function prediction. The project will involve collaboration with biologists for building and refining annotation graphs, and validating patterns to ensure relevance to their research.
The project makes broad contributions to the Arabidopsis thaliana community. PattArAn may assist Arabidopsis curators to manage GO-PO annotations and complement existing tools such as Textpresso and AraNet. It can also be used to bootstrap an annotation database for other plant species given that their genome sequence information is available. The project offers significant research and educational experiences for graduate students (University of Maryland and Iowa) and undergraduate students (St. Bonaventure University). Team members will continue to mentor women and students from under-represented communities, participate in outreach activities, lead a Journal Club, etc. The outcomes from this research project will be disseminated via biology and bioinformatics venues. More information may be obtained at the project website: https://wiki.umiacs.umd.edu/clip/pattaran/.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project aims to develop tools that exploits the wealth of human-curated knowledge in the literature and in the annotation evidence. The tools would benefit biologists who have a range of information discover needs from quickly understanding the functional behavior of a large number of genes to developing a deeper understanding of the relationship of genes within a biological pathway.
In reaching this goal, the collaboration team at St Bonaventure University, University of Iowa and University of Maryland worked together to develop a sentence-retrieval tool, Ferret, and a visualization tool, semEP, to create meaningful clusters of genes and their GO annotations. Both tools were field tested with research data from independent biologists and optimized to make it user-friendly. The SBU PI also used Ferret to examine GO enrichment results for an RNA-seq experiment. It confirmed GO enrichment results on an unexpected defense role of a splicing regulator, serine-arginine rich 45 (SR45), in Arabidopsis, which has never been studied before.
To integrate the above project into the biology curriculum, the SBU PI Designed and implemented a bioinformatics lab exercise to teach students vocabularies and visualizations of human-curated knowledge from the literature and in the annotation evidence. She also designed and implemented a bioinformatics research program that involved for 11 high school sophomores and juniors to work with 4 undergraduate students and 4 faculty at SBU for 2 weeks. With a bioinformatics undergraduate major, the SBU PI designed and implemented a bioinformatics workshop curriculum with a focus on breast cancer for K-12 math and science teachers.
Research results were presented at professional meetings annually. Both the HS student research and teacher workshop were disseminated via WNY STEM HUB, facebook, and LinkedIn. The teacher workshop photos are disseminated as a vimeo slideshow: https://vimeo.com/228492495.
Last Modified: 09/10/2017
Modified by: Xiao-Ning Zhang
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